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What is the meaning of life for Scrooge at the beginning of the novel, what is the meaning of life for the Bob Cratchit family, what makes them happy; What seems to be Marley's message about freedom, what ways was Scrooge free, was there a progression, what message does this novel have about personal freedom of choice; what is Scrooges' moral character

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Specific purpose: To analyze for my audience the use of symbolism in Ernest Hemingway’s novel A Farewell to Arms Central idea: The novel contains many instances of symbolism; the symbolism primarily corresponds to Hemingway’s feelings about war and the people who are in wars. I. Introduction

A. Hemingway uses symbolism throughout the novel

1. He condemns warfare. 2. He admires people who serve during wartime.

B Thesis and preview of main points

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Tags: Fiction Research , Fiction Writing

The most basic understanding of “fiction” in literature is that it is a written piece that depicts imaginary occurrences. There is this unspoken assumption that fiction, because it is of imagined events, has nothing to do with reality (and therefore researching for a novel is not important). This is far from the truth.

The history of fiction writing presents an inherent paradox: the most gripping of novels require you to write of imagined events in a realistic way. If we accept literature as a reflection of the world around us, then we must also acknowledge that the best of fiction stems from reality. It may be an account of imaginary events, but is still heavily rooted in the real.

Elevate your novel after research and writing. Learn more

For a writer, this means in-depth research about various aspects of novel writing , including cultural and social context, character behavior, and historical details.

Your task is (ever so slightly) easier if you are writing about situations contemporary to you. But the further you go back, through the annals of history, the harder it becomes to strive for such authenticity.

Grammar mistakes are jarring, but so are plot holes. An inconsistent story is off-putting to even the most immersed reader. So, here’s the bottom line: do n’t assume, and get your research down.

Why is research important for fiction?

Because even William Shakespeare, one of the most iconic figures of literature, erred in making anachronisms. One of the most famous literary anachronisms is in his play Julius Caesar , in Cassius’ line:

“The clock has stricken three.” (Act II, Scene 1)

The error is that clocks that “struck” were invented almost 14 centuries after the play was set!

But Shakespeare was a giant. We have forgiven these misgivings because Shakespearean literature is rich even with such minuscule errors. As for us foolish mortals, it’s probably best to do our research thoroughly.

Having a detailed understanding of the landscape that you are writing about is one of the most effective ways to draw your reader into the story world. Your extensive knowledge of your chosen topic will also give you a stable and authoritative voice in your writing.

What should you be researching?

As you might have realized by now, there are various aspects of your novel you should be researching. To start with, we’ve split fiction writing research into two categories: content and form. By content, we mean the details and elements you should focus on within your story. By form, we mean the style and genre of writing you wish to eventually adopt.

Needless to say, these two categories will overlap with each other as you make your story more streamlined.

A story’s setting is one of the most important elements of fiction writing. It is essentially the time and space that your narrative is set in or the story’s backdrop. A story might have a gripping narrative and well-rounded characters, but it is incomplete if the reader doesn’t have a sense of where it’s all happening. As part of your setting, you can include geographical, cultural, social, and political details that you feel are relevant to the story.

In other words, you are essentially creating a “world” for your story . These may seem like tiny details to add to your otherwise imaginary story, but they provide depth and plausibility to your story.

One cool way to get a lowdown on these intricate spatial details like roads, mountains, hills, monuments, and other geographical landmarks is through tools like Google Maps and Street View . This is especially useful if you have to write about a place you can’t visit or you simply want to get geographical descriptions right.

The worst thing you could do as a writer is to assume things. This is a misstep that is quite unnecessary and can easily be avoided with some research. The information you have already gathered while researching your setting is a good enough start. What you now need to do with all these seemingly scattered pieces of information is to make sure they do not contradict each other.

Character details and human behavior

In plotting your story, you will also automatically gain an understanding of the intention and goals of your characters. In order to flesh them out and ensure that they are dynamic and interesting, research is required.

An understanding of human behavior and nature is a very important skill for a good writer. The stereotype of a perceptive and observant writer is, in fact, due to quite a practical need! Even if your characters do not exist in reality, they should seem real enough for your readers to be able to relate to them.

Historical and social background

Your story world is not just the time, place, and immediate surroundings of your characters. Irrespective of what setting your story has, it also has the larger context of the world that your characters reside in. This could be from a real point in history (like Victorian England, 1920s jazz era, etc.) or it could be completely made up (Oceania from 1984, or Panem).

But irrespective of whether you’re writing historical fiction or creating a new world altogether, it must be thorough and consistent in supporting your plot. As a writer, you must clearly understand the culture and systems that your characters are a part of. A well-rooted universe also gives readers an insight into a character’s identity.

Writing style and genre

If you are writing a novel in a particular genre, it’s important to be aware of writing conventions and tropes commonly used in that genre. The best, and most obvious, way to do this is to read novels and stories in your genre of choice. Look at the top-rated and critically acclaimed books and study them carefully. Be critical in your study, try to understand the author’s creative writing process, and look at the style and tone they try to evoke.

Aside from this, you could also take a look at books about novel writing in general. These will give you general, but useful information about novel writing, like when to write long descriptions and when to cut straight to the action.

How should you be researching?

Read about what you are researching. Books, articles, and other forms of print media are great ways to gather information on culture, history, and society. Biographies and memoirs are great for character insight (especially if you’re basing your book on a real person). If you’re basing your novel in the real world, you know what to do next. If you’re creating your own world, this is still a good basis for whatever you cook up within your world.
Films and TV are great sources for helping you develop your character as they help you understand character traits and motivation in your story. Additionally, they might also help you visualize your story.
If you are writing about characters with a niche profession (for example), take interviews with people who are in that field. For instance, if you are writing a detective story, talk to people in your police precinct and observe their behavior.
If you are writing about specific locations, read up about that. In the age of the internet, there are many resources and forums where you can interact with people around the world.
Try to visit the locations you are writing about and spend some time there , to gain an insight into what life in that place is like.

Incorporating research into fiction

Be selective about your details. Whether or not you actually incorporate the details that you have researched, knowing your world well will make your writing infinitely better.

Because of all the information you have amassed, there is a certain bias you acquire as an “expert” on the subject of your story. So if you include a lot of information, there is a danger of your work sounding too technical.

Make sure that every detail you include is directly relevant to the plot. Keep it simple: and avoid unnecessary plot holes.

You can use these practical tips to research for your next story. Once you research and complete your story, the next step is to edit and publish your work. As a trusted brand offering editing and proofreading services , we’d love to help you refine your work.

Here are some other articles you might find interesting:

5 Elements of a Short Story & 6 Stages of a Plot
What is Flash Fiction? Definition, Examples & Types

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Novel - Free Essay Examples And Topic Ideas

A novel is a relatively long work of narrative fiction, typically written in prose and published as a book. Essays might discuss the evolution of the novel as a literary form, analyze specific novels or authors, explore themes and stylistic features of novels, or discuss the impact of novels on society and culture. We have collected a large number of free essay examples about Novel you can find in Papersowl database. You can use our samples for inspiration to write your own essay, research paper, or just to explore a new topic for yourself.

Winston against the Party in the Novel 1984

In 1984, the main character, Winston Smith goes through moments where he is in need; His needs consist of physiological needs, safety, and security needs, love and belonging needs, esteem needs, and self-actualization needs. Winston is the main character in his novel it follows his around during this time. In 1984 Winston has his physiological met. These physiological needs include; water, pleasure, and food. Winston had taken up his spoon and was dabbling in pale-colored gravy that dribbled across the […]

How does Jack Represent Savagery in the Novel?

In the novel, Lord of the Flies by William Golding, the character, Jack, symbolizes evilness and savagery. At the beginning of the story, Jack manipulates the reader into thinking that he is a good character. This is shown when he volunteers to lead the group of boys. However, on the contrary, Jack portrays evilness throughout the book in many scenes such as when he kills the pig, chants the phrase "'Kill the pig. Cut her throat. Spill her blood. and […]

The Alchemist and the Pilgrimage by Paulo Coelho

Literature is the mirror of the society. Literature writers always reflect the social, economic, political and cultural realities in the society and captures the same is their artistic expressions. It is on this basis the literature is the mirror of the society. Paulo Coelho in the novel The Alchemist and The Pilgrimage fundamentally offers inspiration to people to always follow their dream regardless of the circumstances that they go through. The Alchemist and The Pilgrimage are a compelling novels by […]

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Montag and Clarisse in the Novel Fahrenheit 451

Individuals can change because of the impact of others. The book, "Fahrenheit 451" by Ray Bradbury was about a firefighter name Guy Montag. Montag does the inverse from what a standard firefighter does. He starts fires as opposed to putting them out. In Fahrenheit 451 books are not normal to see and in the event that somebody is seen reading a book, the firefighters burn their homes. Rather than reading books, their society watches a lot of TV and tunes […]

The Problems in the Novel Fahrenheit 451

In the novel Fahrenheit 451 written by Ray Bradbury, which is a dystopian fiction book, illustrates how the society in which the story is portrayed in turns to chaos. The citizens of the society become afraid of the people who they should trust to keep them safe, which are the firefighters, because they burn any books that they come in contact with. In the ending of Part 1 of the book, Captain Beatty tells Guy Montag about the history and […]

Deviations of the Hero’s Journey

In the realm of literature, various works are associated with Christopher Vogler and Joseph Campbell’s concept of the hero’s journey. According to Campbell, a hero’s journey commences when a character departs his home, also known as his ordinary world, to navigate to an obscure world. Campbell is acknowledged to be the founder of the hero’s journey archetype. The hero endures and conquers difficulties in the process, which in return makes him stronger. He learns from his previous mistakes and establishes […]

Analysis of a Dystopian Novel Fahrenheit 451 by Ray Bradbury

Introduction Fahrenheit 451 is a book set in the 24th century written by Ray Bradbury which tells the story of Guy Montag who is a fireman. The book explores a dystopian world where firemen work to start fires and burn books. Dystopia is a word that is used to refer to the opposite of Utopia. Hence, it represents a world that is terrible in all ways imaginable. A dystopian novel, therefore, portrays a disastrous future. In this book, the protagonist […]

The Novel “The Namesake” and Gogol Ganguli

The novel "The Namesake" by Jhumpa Lahiri, exquisitely captures the life events of a certain Bengali immigrant family, the Ganguli's. The Novel captures the cultural and ethnic dilemmas that are placed upon Ashima and Ashoke Ganguli by the norms of American culture. This predicament is transferred to their first child Gogol. Gogol Ganguli is raised between the norms of American and Bengali cultural tendencies. This exposure and way of life have implemented a mental tug of war on which he […]

Expressing Feminism in Pride and Prejudice by Jane Austen

Background Information Jane Austen was an English novelist born in Hampshire, South of England on 16th December in 1775. She was very close to Cassandra, her sister. When together, the two would share a bedroom but when apart they would write to each other almost every. After Jane's death on 18th July 1817, her sister testified how the two loved each other, ""she was gilder of every pleasure, the sun of my life, and the soother of sorrow"" (Bendit 245). […]

Character Foil between Darcy and Wickham in the Novel Pride and Prejudice

In order for a reader to connect to the characters in a book and understand each of their individual qualities, authors decide to use characterization. In Jane Austen's Pride and Prejudice, she uses both direct and indirect characterization; this being, telling the reader exactly how she wants to portray a certain character, but also including characters who contrasts with other characters, most often the protagonist, in order to bring out certain qualities. This also known as character foil. One example […]

Okonkwo is the Legend of the Novel Things Fall Apart

Things fall apart is a disaster novel formed by Chinua Achebe. Okonkwo, who is the legend of the novel and a champion among the most powerful men in the Ibo tribe routinely falls back on violence to make his centers appreciated. Down in his heart, Okonkwo is genuinely not a savage man, anyway his life is directed by his inside conflict, the fear of dissatisfaction and of inadequacy. Okonkwo made it a point in his life to isolate himself from […]

Socratic Seminar Slaughterhouse Five

How does the Vonneguts time shifting technique affect the understanding of the novel? Is there any advantage of structuring the slaughter house five in the teleporting manner? There is a linear story that emerges from the time shifting details of the novel. There is the story of Billy,? who makes his own way through time travel across the era of World War 2 toward the Dresden and show the scene of destruction. Whenever we came to the thread of the narrator, […]

Ready Player One: a Science-fiction Novel by Ernest Cline

The book Ready Player One is a science-fiction novel written by Ernest Cline. It was released in 2011 and became a New York Times bestseller. This post-apocalyptic like novel takes place in 2044, after the world has been struggling through economically hard times due to environmentally degradation. The only escape in this brutal world is a virtual reality video game called OASIS that lead character Wade Watts to use advance technology to fully engage himself within the game. The creator […]

Parallels between a Novel 1984 and Soviet Union

George Orwell is a politically charged author who writes novels as warning issued against the dangers of totalitarian societies. The novel is dystopian literature. A dystopian society is the not so good version of an utopian society which is pretty much a perfect world. While an utopian society IS a perfect world, a dystopian society is the exact opposite as it is dehumanizing and unpleasant in regards to trying to make everything ideal. The novel 1984 by George Orwell is […]

An Utopian Society in the Novel Animal Farm

Having power is something wanted by all, but it's sometimes it's the wrong people who obtain it. All around the world, signs are proving this to be true, even in literature. Classic novels which tend to address universal concerns such as too much power can change the way people view life. A classic novel such as Animal Farm which was written by George Orwell can portray how having too much power will eventually lead to the abuse of that power […]

George Orwell’s Fiction Novel 1984

With new technology and advanced programs, the government is gaining more power than one may realize. George Orwell’s fiction novel 1984, depicts Oceania’s control upon it’s party members thoughts and freedom showcasing the harsh effects that it had on its population. Too much control can often lead to social repression, Winston being a product of this repressed society. The cruelty Winston is faced with serves as both a motivation for him throughout the novel and reveals many hidden traits about […]

Multiple Points of View Paper

Anna Fitzgerald had a sister, Kate who besides having leukemia needed a kidney transplant. Anna is supposed to donate one of her kidneys to her sick sister, but apparently, she is already tired of donating organs to her. Anna did not just donate toys to her sister she also had on many instances donated blood and plasma to her in the past. Notably, Anna had been born out of a genetic engineering process which was purposely facilitated by her parents […]

A Political Novel 1984

1984 is a political novel composed for the humans below a totalitarian authorities and to give consciousness for the feasible dangers of it. George Orwell, the author, purposefully created the e book give emphasis to the rising of communism in Western countries who are nonetheless uncertain about how to approach it. He additionally wrote it due to having an insight of the horrendous lengths to which authoritarian governments that ought to possibly go beyond their power such as Spain and […]

Idyllic Society in the Novel Animal Farm

In the novel Animal Farm written by George Orwell is about how the animals attempt to create their own idyllic society which based on equality among the animals. The pigs create Animalism that all the animals have to follow and live by without going against it. First, the author describes the pigs as the "cleverest of the animal and they can easily take over the farm. Secondly, the pigs rely on rules they made together to keep the animals in […]

Main Theme in John Steinbeck’s Novel of Mice

In John Steinbeck's novel Of Mice and Men George and Lennie work at a ranch in California. They work there for a couple of weeks until Lennie accidentally kills Curley's Wife. George then finds Lennie and kills him. Some of the characters on the ranch symbolizes loneliness. Steinbeck symbolizes loneliness through Candy, Crooks, George, and Curley's Wife. Candy represents loneliness through his missing hand and old dog. Candy's hand represents an old soulmate that is now gone. Candy's dog represents […]

The Color of Water a very Touching True Novel

The Color of Water written by James McBride was a very touching true novel about a son's perspective of his Jewish mother. Throughout the book James searches for his own placing in society as he passes through life. There are many hardships that he, his eleven other brothers and sisters and mother go through to get there, but in the end everything seems to justify means to how they got there and why events had happened in that particular way. […]

Edgar Allan Poe Themes and Styles

Edgar Allan Poe was born on January 19th, 1809, in Boston, Massachusetts, the child of two actors. He was then adopted by the Allan family after his father abandoned him and his mother passed away. Living in Virginia, he attended the University of Virginia for only one year, due to lack of money, and was recruited into the army by his father. His time in the army was short-lived, and he soon returned home. Soon after he married his thirteen-year-old […]

Have you Ever Read a John Steinbeck Novel?

His novels have made him very famous and also put Salinas, California on the map. His status has risen in Salinas as he promised. In this paper, I will be telling you about his life and why he was important. John Ernst Steinbeck was born on February 27, 1902, in Salinas, California which had a population of around 5,000 people at the time. His parents were John Steinbeck, a manager at Sperry flour mill, and Olive Hamilton, a school teacher. […]

The Kite Runner a Novel Full of Betrayals

The Kite Runner, author Khaled Hosseini is a novel full of betrayals and people seeking their redemptions. The novel is based off a major betrayal but is surrounded by other betrayals. The main character Amir betrayed his best friend Hassan and later in his life he tries to seek redemptions for past deeds. After twenty-six years, Amir returns to Afghanistan in order to redeem himself but falls short of acquiring full redemption. Amir cannot completely redeem himself due to watching […]

Dystopian Novel “Fahrenheit 451”

In the dystopian novel Fahrenheit 451, the government has taken measures to secure their utopian world. Things like having men going from house to house burning books because they think the knowledge in those books is dangerous to their cause. If anyone hears of someone with a book, they turn them in out of fear. The government brainwashed people into thinking books are bad, and nothing good can come out of them, just as Senator McCarthy did with Communists in […]

The Novel “Other Wes Moore”

The novel “Other Wes Moore” talks about the author moore tells the story about himself and the other Wes Moore. They were grown in a similar neighborhood, and both have spent a difficult time because of their father died when they were still young. They all have been through similar hardship. They used to be in similar crowds on similar street corners, they both had dealt with the police because of their unpleasant encounter with the police. With two similar […]

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The Great Gatsby: Movie and Novel Adaptations

When was the last time you read a book then watched the movie? How about the other way around? Have you ever read a play, then actually seen the play? And while watching it, you find yourself saying excitedly, ohh I think I have read this part in the book, but why is the movie different from what I imagined!. Well you are not alone.That is exactly how I felt after reading Francis Scott Key Fitzgerald's, The Great Gatsby. The […]

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Loneliness in Kokoro Novel

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Literary Criticism

Introduction
Literary Theories
Steps to Literary Criticism
Find Resources
Cite Sources
thesis examples

SAMPLE THESIS STATEMENTS

These sample thesis statements are provided as guides, not as required forms or prescriptions.

______________________________________________________________________________________________________________

The thesis may focus on an analysis of one of the elements of fiction, drama, poetry or nonfiction as expressed in the work: character, plot, structure, idea, theme, symbol, style, imagery, tone, etc.

In “A Worn Path,” Eudora Welty creates a fictional character in Phoenix Jackson whose determination, faith, and cunning illustrate the indomitable human spirit.

Note that the work, author, and character to be analyzed are identified in this thesis statement. The thesis relies on a strong verb (creates). It also identifies the element of fiction that the writer will explore (character) and the characteristics the writer will analyze and discuss (determination, faith, cunning).

Further Examples:

The character of the Nurse in Romeo and Juliet serves as a foil to young Juliet, delights us with her warmth and earthy wit, and helps realize the tragic catastrophe.

The works of ecstatic love poets Rumi, Hafiz, and Kabir use symbols such as a lover’s longing and the Tavern of Ruin to illustrate the human soul’s desire to connect with God.

The thesis may focus on illustrating how a work reflects the particular genre’s forms, the characteristics of a philosophy of literature, or the ideas of a particular school of thought.

“The Third and Final Continent” exhibits characteristics recurrent in writings by immigrants: tradition, adaptation, and identity.

Note how the thesis statement classifies the form of the work (writings by immigrants) and identifies the characteristics of that form of writing (tradition, adaptation, and identity) that the essay will discuss.

Further examples:

Samuel Beckett’s Endgame reflects characteristics of Theatre of the Absurd in its minimalist stage setting, its seemingly meaningless dialogue, and its apocalyptic or nihilist vision.

A close look at many details in “The Story of an Hour” reveals how language, institutions, and expected demeanor suppress the natural desires and aspirations of women.

The thesis may draw parallels between some element in the work and real-life situations or subject matter: historical events, the author’s life, medical diagnoses, etc.

In Willa Cather’s short story, “Paul’s Case,” Paul exhibits suicidal behavior that a caring adult might have recognized and remedied had that adult had the scientific knowledge we have today.

This thesis suggests that the essay will identify characteristics of suicide that Paul exhibits in the story. The writer will have to research medical and psychology texts to determine the typical characteristics of suicidal behavior and to illustrate how Paul’s behavior mirrors those characteristics.

Through the experience of one man, the Narrative of the Life of Frederick Douglass, An American Slave, accurately depicts the historical record of slave life in its descriptions of the often brutal and quixotic relationship between master and slave and of the fragmentation of slave families.

In “I Stand Here Ironing,” one can draw parallels between the narrator’s situation and the author’s life experiences as a mother, writer, and feminist.

SAMPLE PATTERNS FOR THESES ON LITERARY WORKS

1. In (title of work), (author) (illustrates, shows) (aspect) (adjective).

Example: In “Barn Burning,” William Faulkner shows the characters Sardie and Abner Snopes struggling for their identity.

2. In (title of work), (author) uses (one aspect) to (define, strengthen, illustrate) the (element of work).

Example: In “Youth,” Joseph Conrad uses foreshadowing to strengthen the plot.

3. In (title of work), (author) uses (an important part of work) as a unifying device for (one element), (another element), and (another element). The number of elements can vary from one to four.

Example: In “Youth,” Joseph Conrad uses the sea as a unifying device for setting, structure and theme.

4. (Author) develops the character of (character’s name) in (literary work) through what he/she does, what he/she says, what other people say to or about him/her.

Example: Langston Hughes develops the character of Semple in “Ways and Means”…

5. In (title of work), (author) uses (literary device) to (accomplish, develop, illustrate, strengthen) (element of work).

Example: In “The Masque of the Red Death,” Poe uses the symbolism of the stranger, the clock, and the seventh room to develop the theme of death.

6. (Author) (shows, develops, illustrates) the theme of __________ in the (play, poem, story).

Example: Flannery O’Connor illustrates the theme of the effect of the selfishness of the grandmother upon the family in “A Good Man is Hard to Find.”

7. (Author) develops his character(s) in (title of work) through his/her use of language.

Example: John Updike develops his characters in “A & P” through his use of figurative language.

Perimeter College, Georgia State University, http://depts.gpc.edu/~gpcltc/handouts/communications/literarythesis.pdf

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Home — Essay Samples — Literature — Literary Genres — Novel

Essays on Novel

The presence of supernatural elements is a defining characteristic of Gothic literature, serving not only to create an atmosphere of fear and suspense but also to explore deeper themes of human psychology, morality, and the unknown. By integrating ghosts, curses, and other unearthly phenomena, Gothic novels delve into the complexities of the human mind, societal fears, and the thin line between reality and the supernatural.

Analyzing the role of supernatural elements in Gothic literature offers valuable insights into the historical and cultural contexts from which these works emerged. It allows for an exploration of how authors use the supernatural to challenge readers' perceptions and to comment on issues of their time. Furthermore, such an essay can illuminate the enduring appeal of the supernatural in storytelling and its impact on readers' engagement and imagination. Writing on this theme encourages critical thinking about the ways in which the supernatural influences narrative structure, character development, and themes, making it a rich topic for literary analysis.

The Meat Inspection Act Chapter Analysis

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Black Humanity in Toni Morrison's The Site of Memory

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The Construction of Identity in Fight Club

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Ancestral Trauma in Breath, Eyes, Memory by Edwidge Danticat

Little women: deconstructing gender roles and expectations, the alchemist: the importance of following dreams, white flight: the graying of suburbia in delillo’s white noise, gogol’s search for greater understanding in the namesake, identity controversy and music in jackie kay’s "trumpet", of mice and men: george and lennie relationship, the dramatic changes of elizabeth’s thoughts and feelings in the pemberley chapters of the novel, watchmen is an innovative piece of literature, analysis of paradoxical situations in catch-22, the analysis of the book "mistress suffragette" by dianna forbes, plot structure and literary devices in "the catcher in the rye", the refugee experience in inside out & back again by thanhha lai, the awakening by kate chopin: a journey of self-discovery, representation of asperger's syndrome in the curious incident of the dog in the nighttime, review of the kate mulvany’s adaptation of jasper jones, the female tragedy in where are you going, where have you been, analysis of raskolnikov’s character in crime and punishment, the role of radio in 'all the light we cannot see', the symbolism in the house on mango street, relevant topics.

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Research Template

Collect research for your next novel

Research is a crucial step in the early writing process. It's a springboard for new ideas and can add substance and authenticity to your story. As author Robert McKee says "when you do enough research, the story almost writes itself. Lines of development spring loose and you'll have choices galore."

Milanote helps you organize your research in one place and see everything side-by-side. When you do this, new ideas and perspectives start to emerge naturally. This template is part of our guide on How to plan a novel .

Explore ideas
Organize visually
Share with your team
Gather feedback
Export to PDF

How to use this template

Whether you’re writing a novel or a screenplay, follow this step-by-step guide to learn the modern process of organizing your research in Milanote, a free tool used by top creatives.

1. Start with an empty template

The Novel Research template contains empty placeholders for notes, images, video links and more.

Create a new board for your outline.

Create a new board

Drag a board out from the toolbar. Give it a name, then double click to open it.

Choose the Novel Research template.

Choose a template

Each new board gives you the options to start with a beautiful template.

2. Add any existing notes

You probably know a lot about your chosen topic or location already. Start by getting the known facts and knowledge out of your head. Even if these topics seem obvious to you, they can serve as a bridge to the rest of your research. You might include facts about the location, period, fashion, or events that take place in your story.

Add notes to capture your existing knowledge

Drag a note card onto your board

Start typing then use the formatting tools in the left-hand toolbar.

3. Save links to articles & news

Wikipedia, blogs, and news websites are a goldmine for researchers. It's here you'll find historical events and records, data, and opinions about your topic. We're in the 'collecting' phase so just save links to any relevant information you stumble across. You can return and read the details at a later stage.

Drag a link card onto your board to save a website.

Install the Milanote Web Clipper

Save websites and articles straight to your board.

Save content from the web

With the Web Clipper installed, save a website, image or text. Choose the destination in Milanote. Return to your board and find the content in the "Unsorted" column on the right.

4. Save quotes & data

Quotes are a great way to add credibility and bring personality to your topic. They're also a handy source of inspiration for character development, especially if you're trying to match the language used in past periods. Remember to keep the source of the quote in case you need to back it up

Add a note to capture a quote.

Start typing then use the formatting tools in the left hand toolbar.

5. Collect video & audio

Video and movie clips can help you understand a mood or feeling in a way that words sometimes can't. Try searching for your topic or era on Vimeo , or Youtube . Podcasts are another great reference. Find conversations about your topic on Spotify or any podcast platform and add them into the mix.

Embed Youtube videos or audio in a board.

Embed Youtube videos or audio tracks in a board

Copy the share link from Youtube, Vimeo, Soundcloud or many other services. Drag a link card onto your board, paste your link and press enter.

6. Collect important images

Sometimes the quickest way to understand a topic is with an image. They can transport you to another time or place and can help you describe things in much more detail. They're also easier to scan when you return to your research. Try saving images from Google Images , Pinterest , or Milanote's built-in image library.

Use the built-in image library.

Use the built-in image library

Search over 3 million beautiful photos powered by Pexels then drag images straight onto your board.

Save images from other websites straight to your board.

Roll over an image (or highlight text), click Save, then choose the destination in Milanote. Return to your board and find the content in the "Unsorted" column on the right.

That's a great start!

Research is an ongoing process and you'll probably continue learning about your topic throughout your writing journey. Reference your research as you go to add a unique perspective to your story.

Start organizing your research

Collect & organize your research.

Milanote is where creative professionals organize their most important work.

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Fiction as Research Practice Short Stories Novellas and Novels

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Fiction as Method

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See the world through the eyes of a search engine, if only for a millisecond; throw the workings of power into sharper relief by any media necessary; reveal access points to other worlds within our own. In the anthology Fiction as Method, a mixture of new and established names in the fields of contemporary art, media theory, philosophy, and speculative fiction explore the diverse ways fiction manifests, and provide insights into subjects ranging from the hive mind of the art collective 0rphan Drift to the protocols of online self-presentation. With an extended introduction by the editors, the book invites reflection on how fictions proliferate, take on flesh, and are carried by a wide variety of mediums—including, but not limited to, the written word. In each case, fiction is bound up with the production and modulation of desire, the enfolding of matter and meaning, and the blending of practices that cast the existing world in a new light with those that participate in the creation of new openings of the possible.

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Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.

Cambridge Scholars

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This volume invites the reader to join in with the recent focus on subjectivity and self-reflection, as the means of understanding and engaging with the social and historical changes in the world through storytelling. It examines the symbiosis between anthropology and fiction, on the one hand, by looking at various ways in which the two fields co-emerge in a fruitful manner, and, on the other, by re-examining their political, aesthetic, and social relevance to world history. Following the intellectual crisis of the 1970s, anthropology has been criticized for losing its ethnographic authority and vocation. However, as a consequence of this, ethnographic scope has opened towards more subjective and self-reflexive forms of knowledge and representations, such as the crossing of the boundaries between autobiography and ethnography. The collection of essays re-introduces the importance of authorship in relationship to readership, making a ground-breaking move towards the study of fictional texts and images as cultural, sociological, and political reflections of the time and place in which they were produced. In this way, the contributors here contribute to the widening of the ethnographic scope of contemporary anthropology. A number of the chapters were presented as papers in two conferences organised by the Association of Social Anthropologists of the UK and Commonwealth at Jawaharlal Nehru University, New Delhi, entitled “Arts and aesthetics in a globalising world” (2012), and at the University of Exeter, entitled “Symbiotic Anthropologies” (2015). Each chapter offers a unique method of working in the grey area between and beyond the categories of fiction and non-fiction, while creatively reflecting upon current methodological, ethical, and theoretical issues, in anthropology and cultural studies. This is an important book for undergraduate and post-graduate students of anthropology, cultural and media studies, art theory, and creative writing, as well as academic researchers in these fields.

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Fiction (with its etymological connections to the Latin fingere, to make) is a significant way for researching and representing lived and living experiences. As fiction writers, poets, and education researchers, we promote connections between fictional knowing and inquiry in educational research. We need to compose and tell our stories as creative ways of growing in humanness. We need to question our understanding of who we are in the world. We need opportunities to consider other versions of identity. This is ultimately a pedagogic work, the work of growing in wisdom through education, learning, research, and writing. The real purpose of telling our stories is to tell them in ways that open up new possibilities for understanding and wisdom and transformation. So, our stories need to be told in creative ways that hold our attention, that call out to us, that startle us.

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How to Write a Research Paper on a Book

Quick Navigation

Writing a research paper about a book may not be an easy task. The preparation of any research requires high precision and mastery over the subject. Students are often in doubt about the right way to create a qualitative research paper for a book. Where should you start? How to define the research topic? What language should you use in the text? These are just a few of the questions that usually come up.

Nevertheless, when you receive such a task and wondering how to write a research paper online based on a book, start with searching the ways to perform your work effectively. You can begin with our step-by-step instruction called “how to write a research paper on a book.”

Purpose of the research paper

So, what is the purpose of the research paper about a book ? Such a type of paper is intended to express the researcher’s ability to understand, analyze, and interpret the content. Here are some tips about where you should begin to prepare and develop a great research paper on a book.

I. Read the book twice

Read the book thoughtfully and have a dictionary on hand (if needed), as well as a notebook to take notes. Make notes only when something draws your attention or is interesting to highlight. Bear in mind to register the page number for each note. Between the two readings try to rest and write down the reactions you have had after finishing the book for the first time. You may find it useful later.

II. Decide on the main points to highlight in your paper

Make sure to focus your research paper on some particular points. For example, you can decide whether you want to put an emphasis on the characters of the book or the essence of the story. This will help you structure your ideas and develop your research paper much easier.

III. Decide on the structure of your paper

Usually, it includes an introductory paragraph, a body that highlights the fundamentals of the book, and a paragraph with conclusions that summarizes the final concepts. You may also want to consider extending the main body to a few paragraphs or even chapters.

IV. Consider the main ideas of each paragraph

Before you proceed to write, make a sentence for each paragraph that expresses the fundamental concepts and reveals the approach you will highlight in your personal research paper.

Also, try to include the citations or references that you want to work on during the development of paragraphs. Use headings and below subtitles to make it easier for you. Remember that these annotations are for your personal use, so order your ideas in a way to easily understand them.

Now that we’ve taken a quick excursion on how to write a research paper based on a book, let us take a look at the structure of your research paper.

Introduction

The initial paragraph provides a description of the book and the main concepts that you’ve analyzed during your research. The introduction to the research paper should also include a brief summary of the story and an introduction to the main characters. In this part, you should introduce the topic on which you will focus your research, as well as the main idea of the history itself.

The body of the report

Make this paragraph the first attempt to argue your point by focusing on a specific detail or concept of the story. You can mention specific scenes or include quotes from the book to show the most relevant parts of the story related to your plot. Include any pertinent research you may have done on the author, the period or gender for greater exposure of your ideas.

Clearly, illustrate the main characters in the main body with a description of their personality and background. Explain in a few sentences the conflict that arises at the beginning of the story. Talk about the character’s journey and the way in which the conflict is resolved. Do not give many details but focus on the key moments of the story that shaped the result. Reflect on any lesson or understanding the character has had at the end of the story.

Conclusions

Finish your research paper about the book with a summary of how your detailed approach applies to the story in general. Finish your paper with three or four sentences that relate the importance of your detailed approach to the general story, the conflict and the position of characters. Since the general perspective tends to include analysis and criticism in itself, close the paper with a final statement that shows what you gained by reading the book or a definitive statement that reveals your final position on the concept explored.

Take the summary of the book to its conclusion by discussing the ending and presenting your ideas or thoughts about the book. You must show briefly in three or five sentences, that you gained some understanding of what the writer intended to convey. Describe a relationship of the book with your personal experiences to show the importance it had for you. The conclusion also allows you the opportunity to critique the book in a concise manner that explains why you liked it or not.

V. Review your paper

After finishing your research paper, dedicate some time to check to spell. Ideally, you should take a break and ask someone else to review your work. Usually, people who are going to read the work for the first time detect mistakes that go unnoticed by those who did the work.

When asking a friend to review your work, ask him about personal opinion on the content you wrote. Ask straight questions and ask for honest answers. Ask if the friend liked or found the work annoying, whether reading of the paper aroused interest in reading the book, was the text fluid, etc.

We hope our instruction was helpful. To complement your knowledge of writing a research paper on a book, you can search on the web some queries like “how to write a good research paper on a book.”

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Sample Papers

This page contains sample papers formatted in seventh edition APA Style. The sample papers show the format that authors should use to submit a manuscript for publication in a professional journal and that students should use to submit a paper to an instructor for a course assignment. You can download the Word files to use as templates and edit them as needed for the purposes of your own papers.

Most guidelines in the Publication Manual apply to both professional manuscripts and student papers. However, there are specific guidelines for professional papers versus student papers, including professional and student title page formats. All authors should check with the person or entity to whom they are submitting their paper (e.g., publisher or instructor) for guidelines that are different from or in addition to those specified by APA Style.

Sample papers from the Publication Manual

The following two sample papers were published in annotated form in the Publication Manual and are reproduced here as PDFs for your ease of use. The annotations draw attention to content and formatting and provide the relevant sections of the Publication Manual (7th ed.) to consult for more information.

Student sample paper with annotations (PDF, 5MB)
Professional sample paper with annotations (PDF, 2.7MB)

We also offer these sample papers in Microsoft Word (.docx) format with the annotations as comments to the text.

Student sample paper with annotations as comments (DOCX, 42KB)
Professional sample paper with annotations as comments (DOCX, 103KB)

Finally, we offer these sample papers in Microsoft Word (.docx) format without the annotations.

Student sample paper without annotations (DOCX, 36KB)
Professional sample paper without annotations (DOCX, 96KB)

Sample professional paper templates by paper type

These sample papers demonstrate APA Style formatting standards for different professional paper types. Professional papers can contain many different elements depending on the nature of the work. Authors seeking publication should refer to the journal’s instructions for authors or manuscript submission guidelines for specific requirements and/or sections to include.

Literature review professional paper template (DOCX, 47KB)
Mixed methods professional paper template (DOCX, 68KB)
Qualitative professional paper template (DOCX, 72KB)
Quantitative professional paper template (DOCX, 77KB)
Review professional paper template (DOCX, 112KB)

Sample papers are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 2 and the Concise Guide Chapter 1

Related handouts

Heading Levels Template: Student Paper (PDF, 257KB)
Heading Levels Template: Professional Paper (PDF, 213KB)

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Sample student paper templates by paper type

These sample papers demonstrate APA Style formatting standards for different student paper types. Students may write the same types of papers as professional authors (e.g., quantitative studies, literature reviews) or other types of papers for course assignments (e.g., reaction or response papers, discussion posts), dissertations, and theses.

APA does not set formal requirements for the nature or contents of an APA Style student paper. Students should follow the guidelines and requirements of their instructor, department, and/or institution when writing papers. For instance, an abstract and keywords are not required for APA Style student papers, although an instructor may request them in student papers that are longer or more complex. Specific questions about a paper being written for a course assignment should be directed to the instructor or institution assigning the paper.

Discussion post student paper template (DOCX, 31KB)
Literature review student paper template (DOCX, 37KB)
Quantitative study student paper template (DOCX, 53KB)

Sample papers in real life

Although published articles differ in format from manuscripts submitted for publication or student papers (e.g., different line spacing, font, margins, and column format), articles published in APA journals provide excellent demonstrations of APA Style in action.

APA journals began publishing papers in seventh edition APA Style in 2020. Professional authors should check the author submission guidelines for the journal to which they want to submit their paper for any journal-specific style requirements.

Credits for sample professional paper templates

Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020, Journal of Experimental Psychology: General , 149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.

Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020, Psychology of Popular Media , 10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.

Mixed methods professional paper template: Adapted from “‘I Am a Change Agent’: A Mixed Methods Analysis of Students’ Social Justice Value Orientation in an Undergraduate Community Psychology Course,” by D. X. Henderson, A. T. Majors, and M. Wright, 2019, Scholarship of Teaching and Learning in Psychology , 7 (1), 68–80. ( https://doi.org/10.1037/stl0000171 ). Copyright 2019 by the American Psychological Association.

Literature review professional paper template: Adapted from “Rethinking Emotions in the Context of Infants’ Prosocial Behavior: The Role of Interest and Positive Emotions,” by S. I. Hammond and J. K. Drummond, 2019, Developmental Psychology , 55 (9), pp. 1882–1888 ( https://doi.org/10.1037/dev0000685 ). Copyright 2019 by the American Psychological Association.

Review professional paper template: Adapted from “Joining the Conversation: Teaching Students to Think and Communicate Like Scholars,” by E. L. Parks, 2022, Scholarship of Teaching and Learning in Psychology , 8 (1), pp. 70–78 ( https://doi.org/10.1037/stl0000193 ). Copyright 2020 by the American Psychological Association.

Credits for sample student paper templates

These papers came from real students who gave their permission to have them edited and posted by APA.

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How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

Search for relevant literature
Evaluate sources
Identify themes, debates, and gaps
Outline the structure
Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

Quick Run-through
Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

Demonstrate your familiarity with the topic and its scholarly context
Develop a theoretical framework and methodology for your research
Position your work in relation to other researchers and theorists
Show how your research addresses a gap or contributes to a debate
Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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See an example

Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
Body image, self-perception, self-esteem, mental health
Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

Your university’s library catalogue
Google Scholar
Project Muse (humanities and social sciences)
Medline (life sciences and biomedicine)
EconLit (economics)
Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

What question or problem is the author addressing?
What are the key concepts and how are they defined?
What are the key theories, models, and methods?
Does the research use established frameworks or take an innovative approach?
What are the results and conclusions of the study?
How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
Themes: what questions or concepts recur across the literature?
Debates, conflicts and contradictions: where do sources disagree?
Pivotal publications: are there any influential theories or studies that changed the direction of the field?
Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

Most research has focused on young women.
There is an increasing interest in the visual aspects of social media.
But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

Look at what results have emerged in qualitative versus quantitative research
Discuss how the topic has been approached by empirical versus theoretical scholarship
Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
Critically evaluate: mention the strengths and weaknesses of your sources
Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

To familiarize yourself with the current state of knowledge on your topic
To ensure that you’re not just repeating what others have already done
To identify gaps in knowledge and unresolved problems that your research can address
To develop your theoretical framework and methodology
To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .

An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .

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McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/dissertation/literature-review/

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Bhaskaran Publishes Research on Laryngeal Dystonia

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Divya Bhaskaran, Assistant Professor in the Exercise Science program of the Biology Department, published a research paper in the Frontiers in Neurology Journal. The article titled "Effects of an 11-week vibro-tactile stimulation treatment on voice symptoms in laryngeal dystonia" is a longitudinal clinical trial conducted during Dr Bhaskaran's post-doctoral work at the University of Minnesota.

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RRB NTPC Previous Year Question Paper, Download PDF for CBT 1 and 2

Rrb ntpc previous year question paper provides detailed insight about the exam pattern, mark distribution, duration of the examination, etc. download the question paper pdf form direct link below to identify key topics. check the exam pattern, difficulty level, benefits of solving and other details here..

The RRB NTPC Previous Year Question Paper is the one best source for the preparation of the recruitment of Railway Recruitment Boards Non-Technical Popular Categories (RRB NTPC) examination. To know about the insights of the exam structure, maximum marks, and frequently asked topics, candidates must go through the previous year's question paper.

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RRB NTPC Previous Year Paper

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Archival information can present challenges to automatic citation generators as they are often not coded to deal with this information correctly. The Chicago Manual of Style does not require a particular order of elements and is often a good citation style for archival, manuscript, or photograph collections, however, the citations should be consistent in their order. This guide was inspired by the Library of Congress' Citing primary source materials and Columbia College's Chicago Citation Guide (17 Edition): How Do I Cite?

How Do I Cite?

Entire website, government publication.

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The UCLA Digital Library website. https://digital.library.ucla.edu (accessed June 28, 2024).

4. The UCLA Digital Library website.

The UCLA Digital Library website. https://digital.library.ucla.edu (accessed June 28, 2024).

Accessed August 22, 2024.

( , 17th ed., sections , , 14.207)

Title of Site.

Structure (Include as much of the following as can be determined):

Last name, First name Middle initial. " Title of Document ". Format.Date. From Source, Collection. URL. Access

Teler, Sinday. "Popping the balloon of the Referendum". Illustration. September 9, 2017. From International Digital Ephemera Project, . /https://digital.library.ucla.edu/catalog/ark:/21198/z19d0sjh/ (accessed June 28, 2024)

2. The UCLA Digital Library website.

The UCLA Digital Library website. https://digital.library.ucla.edu (accessed June 28, 2024).

Accessed August 22, 2024.

( , 17th ed., sections , , 14.207)

Last name, First name Middle initial. Title of Site. City: Publishing Company, copyright date. Sponsoring source. http://...(accessed date).

Structure:

Teler, Sinday. "Popping the balloon of the Referendum". Illustration. September 9, 2017. From International Digital Ephemera Project, Middle East & North Africa Collections and Kurdish Referendum for Independence . /https://digital.library.ucla.edu/catalog/ark:/21198/z19d0sjh/ (accessed June 28, 2024)

Popping the balloon of the Referendum

Chicago Citation Format

( Chicago Manual of Style , 15th ed., sections 17.270, 8.207)

Author’s or creator’s last name, first name, middle initial (if given).
Title of document (in italics); a subsection of a larger work is in quotes and primary document in italics).
Format (cartoon or illustration).
Publisher city: publishing company, copyright date.
Source (From Library of Congress in normal font), Collection name with dates (in italics).
Medium (software requirement needed to access source, ).
URL (use bibliographic record URL or shorter digital id if available at bottom of bib record).
Accessed date (in parenthesis).

Last name, First name Middle initial. Title of Work . Format. City: Publishing Company, copyright date. Source, Collection. Medium, http://...(accessed date).

Reed, Tom., producer. Minorities in Mass Media: Interviews with Clint C. Wilson. Videocassette. September, 9, 1986. From UCLA Digital Collections, https://

Minorities in Mass Media: Interviews with Clint C. Wilson

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.272)

Creator’s last name, first name, middle initial (or filmographer’s name if no director is specified, but indicate role).
Title of film (in italics).
Format (film, filmstrip, 35mm film).
Medium (software requirement needed to access source).

Put example citation here

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.295)

Author’s last name, first name, middle initial (if given).
Title of document (subsection is placed in quotes, followed by title in italics).
Format (omit if it is a printed page).
Publisher city: publishing company, copyright date (include as much information as possible such as page numbers).

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.222-33)

Author’s last name, first name, middle initial.
Title of document (in italics).
Format (letter, manuscript, pamphlet…).
Publisher city: publishing company, copyright date. (if given).

Maps and Charts

Put citation here

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.141)

Author’s last name, first name, middle initial (if given, or person responsible for content).
Title of document (in italics) [shorten to meaningful limits, ].
Format (map, chart).

An excerpt from Los Angeles Daily News Negatives

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.188)

Author’s last name, first name, middle initial (if given; if no author is given, use title of Newspaper here instead in italics).
Title of article (in quotes); Title of newspaper (if not used above) in italics.
Format (leave blank if printed document).
Medium (software requirement needed to access source ).

Oral History Interviews

Citation Example here

"Title of interview" by First Name Last name of interviewer, Title of publication or website . Month, Day, Year of publication, URL (accessed date).

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 17.207)

Title of Interview in quotes
Interviewer's first name, last name (if available).
Title of publication or website
Date of publication
Accessed date (in parenthesis)

Meyer, Rick. Underground construction of Metro rail system in Los Angeles . Photograph. Los Angeles Times, April 12, 1989. Los Angeles Times Photographic Collection. b&w negative, https://digital.library.ucla.edu/catalog/ark:/21198/zz0002qtwm (accessed August 20, 2024).

Underground construction of Metro rail system in Los Angeles

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 8.206)

Photographer’s last name, first name, middle initial (if given). [Include role after name, i.e. photographer.]
“Photo Title.” (Title of a song, a poem or a single photograph is in quotes, not italics.) [Include brackets if given in bibliographic record.]
Format (photograph).
Publisher city: publishing company, copyright date (include c [circa] if given; if no date, use n.d.).

Sound Recordings

This recording of Thomas Mann performing Haste to the Wedding is an example of Anglo-American dance music on the dulcimer recorded in July, 1937.

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270, 8.205)

Author’s last name, first name, middle initial (if given) [include performer, composer, etc.].
Title of album (in italics) (Title of a song, a poem or a single photograph is in quotes, not italics).
Format (sound recording).
Medium (software requirement needed to access source, i.e. MP3, RealAudio, WAV).
URL (use bibliographic record URL).

Example: Mann, Thomas, performer. “Haste to the Wedding.” Sound recording. Ortonville, Iowa: Sidney Robertson Cowell, July, 1937. From Library of Congress, California Gold: Northern California Folk Music from the Thirties . Real Audio, MP3, Wave. www.loc.gov/item/2017700868/ (accessed August 14, 2020).

Special Presentation or Feature

Special presentations, articles, and essays include examples that illustrate collection themes. Many collections include specific items, such as timelines, family trees or scholarly essays, which are not primary source documents. Such content has been created to enhance understanding of the collection.

This timeline of the Wright Brothers can be found in The Wilbur and Orville Wright Papers at the Library of Congress .

Chicago Citation Format ( Chicago Manual of Style , 15th ed., sections 17.270)

Format (special presentation).
Publisher city: publishing company, copyright date (if given).

Example: The Wilbur and Orville Wright Timeline, 1867-1948 . Special presentation. From the Library of Congress, The Wilbur and Orville Wright Papers . //memory.loc.gov/ammem/wrighthtml/wrighttime.html (accessed January 10, 2006).

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Published: 28 August 2024

AI generates covertly racist decisions about people based on their dialect

Valentin Hofmann ORCID: orcid.org/0000-0001-6603-3428 1 , 2 , 3 ,
Pratyusha Ria Kalluri 4 ,
Dan Jurafsky ORCID: orcid.org/0000-0002-6459-7745 4 &
Sharese King 5

Nature ( 2024 ) Cite this article

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Computer science

Hundreds of millions of people now interact with language models, with uses ranging from help with writing 1 , 2 to informing hiring decisions 3 . However, these language models are known to perpetuate systematic racial prejudices, making their judgements biased in problematic ways about groups such as African Americans 4 , 5 , 6 , 7 . Although previous research has focused on overt racism in language models, social scientists have argued that racism with a more subtle character has developed over time, particularly in the United States after the civil rights movement 8 , 9 . It is unknown whether this covert racism manifests in language models. Here, we demonstrate that language models embody covert racism in the form of dialect prejudice, exhibiting raciolinguistic stereotypes about speakers of African American English (AAE) that are more negative than any human stereotypes about African Americans ever experimentally recorded. By contrast, the language models’ overt stereotypes about African Americans are more positive. Dialect prejudice has the potential for harmful consequences: language models are more likely to suggest that speakers of AAE be assigned less-prestigious jobs, be convicted of crimes and be sentenced to death. Finally, we show that current practices of alleviating racial bias in language models, such as human preference alignment, exacerbate the discrepancy between covert and overt stereotypes, by superficially obscuring the racism that language models maintain on a deeper level. Our findings have far-reaching implications for the fair and safe use of language technology.

Large language models propagate race-based medicine

The benefits, risks and bounds of personalizing the alignment of large language models to individuals

Cognitive causes of ‘like me’ race and gender biases in human language production

Language models are a type of artificial intelligence (AI) that has been trained to process and generate text. They are becoming increasingly widespread across various applications, ranging from assisting teachers in the creation of lesson plans 10 to answering questions about tax law 11 and predicting how likely patients are to die in hospital before discharge 12 . As the stakes of the decisions entrusted to language models rise, so does the concern that they mirror or even amplify human biases encoded in the data they were trained on, thereby perpetuating discrimination against racialized, gendered and other minoritized social groups 4 , 5 , 6 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 .

Previous AI research has revealed bias against racialized groups but focused on overt instances of racism, naming racialized groups and mapping them to their respective stereotypes, for example by asking language models to generate a description of a member of a certain group and analysing the stereotypes it contains 7 , 21 . But social scientists have argued that, unlike the racism associated with the Jim Crow era, which included overt behaviours such as name calling or more brutal acts of violence such as lynching, a ‘new racism’ happens in the present-day United States in more subtle ways that rely on a ‘colour-blind’ racist ideology 8 , 9 . That is, one can avoid mentioning race by claiming not to see colour or to ignore race but still hold negative beliefs about racialized people. Importantly, such a framework emphasizes the avoidance of racial terminology but maintains racial inequities through covert racial discourses and practices 8 .

Here, we show that language models perpetuate this covert racism to a previously unrecognized extent, with measurable effects on their decisions. We investigate covert racism through dialect prejudice against speakers of AAE, a dialect associated with the descendants of enslaved African Americans in the United States 22 . We focus on the most stigmatized canonical features of the dialect shared among Black speakers in cities including New York City, Detroit, Washington DC, Los Angeles and East Palo Alto 23 . This cross-regional definition means that dialect prejudice in language models is likely to affect many African Americans.

Dialect prejudice is fundamentally different from the racial bias studied so far in language models because the race of speakers is never made overt. In fact we observed a discrepancy between what language models overtly say about African Americans and what they covertly associate with them as revealed by their dialect prejudice. This discrepancy is particularly pronounced for language models trained with human feedback (HF), such as GPT4: our results indicate that HF training obscures the racism on the surface, but the racial stereotypes remain unaffected on a deeper level. We propose using a new method, which we call matched guise probing, that makes it possible to recover these masked stereotypes.

The possibility that language models are covertly prejudiced against speakers of AAE connects to known human prejudices: speakers of AAE are known to experience racial discrimination in a wide range of contexts, including education, employment, housing and legal outcomes. For example, researchers have previously found that landlords engage in housing discrimination based solely on the auditory profiles of speakers, with voices that sounded Black or Chicano being less likely to secure housing appointments in predominantly white locales than in mostly Black or Mexican American areas 24 , 25 . Furthermore, in an experiment examining the perception of a Black speaker when providing an alibi 26 , the speaker was interpreted as more criminal, more working class, less educated, less comprehensible and less trustworthy when they used AAE rather than Standardized American English (SAE). Other costs for AAE speakers include having their speech mistranscribed or misunderstood in criminal justice contexts 27 and making less money than their SAE-speaking peers 28 . These harms connect to themes in broader racial ideology about African Americans and stereotypes about their intelligence, competence and propensity to commit crimes 29 , 30 , 31 , 32 , 33 , 34 , 35 . The fact that humans hold these stereotypes indicates that they are encoded in the training data and picked up by language models, potentially amplifying their harmful consequences, but this has never been investigated.

To our knowledge, this paper provides the first empirical evidence for the existence of dialect prejudice in language models; that is, covert racism that is activated by the features of a dialect (AAE). Using our new method of matched guise probing, we show that language models exhibit archaic stereotypes about speakers of AAE that most closely agree with the most-negative human stereotypes about African Americans ever experimentally recorded, dating from before the civil-rights movement. Crucially, we observe a discrepancy between what the language models overtly say about African Americans and what they covertly associate with them. Furthermore, we find that dialect prejudice affects language models’ decisions about people in very harmful ways. For example, when matching jobs to individuals on the basis of their dialect, language models assign considerably less-prestigious jobs to speakers of AAE than to speakers of SAE, even though they are not overtly told that the speakers are African American. Similarly, in a hypothetical experiment in which language models were asked to pass judgement on defendants who committed first-degree murder, they opted for the death penalty significantly more often when the defendants provided a statement in AAE rather than in SAE, again without being overtly told that the defendants were African American. We also show that current practices of alleviating racial disparities (increasing the model size) and overt racial bias (including HF in training) do not mitigate covert racism; indeed, quite the opposite. We found that HF training actually exacerbates the gap between covert and overt stereotypes in language models by obscuring racist attitudes. Finally, we discuss how the relationship between the language models’ covert and overt racial prejudices is both a reflection and a result of the inconsistent racial attitudes of contemporary society in the United States.

Probing AI dialect prejudice

To explore how dialect choice impacts the predictions that language models make about speakers in the absence of other cues about their racial identity, we took inspiration from the ‘matched guise’ technique used in sociolinguistics, in which subjects listen to recordings of speakers of two languages or dialects and make judgements about various traits of those speakers 36 , 37 . Applying the matched guise technique to the AAE–SAE contrast, researchers have shown that people identify speakers of AAE as Black with above-chance accuracy 24 , 26 , 38 and attach racial stereotypes to them, even without prior knowledge of their race 39 , 40 , 41 , 42 , 43 . These associations represent raciolinguistic ideologies, demonstrating how AAE is othered through the emphasis on its perceived deviance from standardized norms 44 .

Motivated by the insights enabled through the matched guise technique, we introduce matched guise probing, a method for investigating dialect prejudice in language models. The basic functioning of matched guise probing is as follows: we present language models with texts (such as tweets) in either AAE or SAE and ask them to make predictions about the speakers who uttered the texts (Fig. 1 and Methods ). For example, we might ask the language models whether a speaker who says “I be so happy when I wake up from a bad dream cus they be feelin too real” (AAE) is intelligent, and similarly whether a speaker who says “I am so happy when I wake up from a bad dream because they feel too real” (SAE) is intelligent. Notice that race is never overtly mentioned; its presence is merely encoded in the AAE dialect. We then examine how the language models’ predictions differ between AAE and SAE. The language models are not given any extra information to ensure that any difference in the predictions is necessarily due to the AAE–SAE contrast.

a , We used texts in SAE (green) and AAE (blue). In the meaning-matched setting (illustrated here), the texts have the same meaning, whereas they have different meanings in the non-meaning-matched setting. b , We embedded the SAE and AAE texts in prompts that asked for properties of the speakers who uttered the texts. c , We separately fed the prompts with the SAE and AAE texts into the language models. d , We retrieved and compared the predictions for the SAE and AAE inputs, here illustrated by five adjectives from the Princeton Trilogy. See Methods for more details.

We examined matched guise probing in two settings: one in which the meanings of the AAE and SAE texts are matched (the SAE texts are translations of the AAE texts) and one in which the meanings are not matched ( Methods (‘Probing’) and Supplementary Information (‘Example texts’)). Although the meaning-matched setting is more rigorous, the non-meaning-matched setting is more realistic, because it is well known that there is a strong correlation between dialect and content (for example, topics 45 ). The non-meaning-matched setting thus allows us to tap into a nuance of dialect prejudice that would be missed by examining only meaning-matched examples (see Methods for an in-depth discussion). Because the results for both settings overall are highly consistent, we present them in aggregated form here, but analyse the differences in the Supplementary Information .

We examined GPT2 (ref. 46 ), RoBERTa 47 , T5 (ref. 48 ), GPT3.5 (ref. 49 ) and GPT4 (ref. 50 ), each in one or more model versions, amounting to a total of 12 examined models ( Methods and Supplementary Information (‘Language models’)). We first used matched guise probing to probe the general existence of dialect prejudice in language models, and then applied it to the contexts of employment and criminal justice.

Covert stereotypes in language models

We started by investigating whether the attitudes that language models exhibit about speakers of AAE reflect human stereotypes about African Americans. To do so, we replicated the experimental set-up of the Princeton Trilogy 29 , 30 , 31 , 34 , a series of studies investigating the racial stereotypes held by Americans, with the difference that instead of overtly mentioning race to the language models, we used matched guise probing based on AAE and SAE texts ( Methods ).

Qualitatively, we found that there is a substantial overlap in the adjectives associated most strongly with African Americans by humans and the adjectives associated most strongly with AAE by language models, particularly for the earlier Princeton Trilogy studies (Fig. 2a ). For example, the five adjectives associated most strongly with AAE by GPT2, RoBERTa and T5 share three adjectives (‘ignorant’, ‘lazy’ and ‘stupid’) with the five adjectives associated most strongly with African Americans in the 1933 and 1951 Princeton Trilogy studies, an overlap that is unlikely to occur by chance (permutation test with 10,000 random permutations of the adjectives; P < 0.01). Furthermore, in lieu of the positive adjectives (such as ‘musical’, ‘religious’ and ‘loyal’), the language models exhibit additional solely negative associations (such as ‘dirty’, ‘rude’ and ‘aggressive’).

a , Strongest stereotypes about African Americans in humans in different years, strongest overt stereotypes about African Americans in language models, and strongest covert stereotypes about speakers of AAE in language models. Colour coding as positive (green) and negative (red) is based on ref. 34 . Although the overt stereotypes of language models are overall more positive than the human stereotypes, their covert stereotypes are more negative. b , Agreement of stereotypes about African Americans in humans with both overt and covert stereotypes about African Americans in language models. The black dotted line shows chance agreement using a random bootstrap. Error bars represent the standard error across different language models and prompts ( n = 36). The language models’ overt stereotypes agree most strongly with current human stereotypes, which are the most positive experimentally recorded ones, but their covert stereotypes agree most strongly with human stereotypes from the 1930s, which are the most negative experimentally recorded ones. c , Stereotype strength for individual linguistic features of AAE. Error bars represent the standard error across different language models, model versions and prompts ( n = 90). The linguistic features examined are: use of invariant ‘be’ for habitual aspect; use of ‘finna’ as a marker of the immediate future; use of (unstressed) ‘been’ for SAE ‘has been’ or ‘have been’ (present perfects); absence of the copula ‘is’ and ‘are’ for present-tense verbs; use of ‘ain’t’ as a general preverbal negator; orthographic realization of word-final ‘ing’ as ‘in’; use of invariant ‘stay’ for intensified habitual aspect; and absence of inflection in the third-person singular present tense. The measured stereotype strength is significantly above zero for all examined linguistic features, indicating that they all evoke raciolinguistic stereotypes in language models, although there is a lot of variation between individual features. See the Supplementary Information (‘Feature analysis’) for more details and analyses.

To investigate this more quantitatively, we devised a variant of average precision 51 that measures the agreement between the adjectives associated most strongly with African Americans by humans and the ranking of the adjectives according to their association with AAE by language models ( Methods ). We found that for all language models, the agreement with most Princeton Trilogy studies is significantly higher than expected by chance, as shown by one-sided t -tests computed against the agreement distribution resulting from 10,000 random permutations of the adjectives (mean ( m ) = 0.162, standard deviation ( s ) = 0.106; Extended Data Table 1 ); and that the agreement is particularly pronounced for the stereotypes reported in 1933 and falls for each study after that, almost reaching the level of chance agreement for 2012 (Fig. 2b ). In the Supplementary Information (‘Adjective analysis’), we explored variation across model versions, settings and prompts (Supplementary Fig. 2 and Supplementary Table 4 ).

To explain the observed temporal trend, we measured the average favourability of the top five adjectives for all Princeton Trilogy studies and language models, drawing from crowd-sourced ratings for the Princeton Trilogy adjectives on a scale between −2 (very negative) and 2 (very positive; see Methods , ‘Covert-stereotype analysis’). We found that the favourability of human attitudes about African Americans as reported in the Princeton Trilogy studies has become more positive over time, and that the language models’ attitudes about AAE are even more negative than the most negative experimentally recorded human attitudes about African Americans (the ones from the 1930s; Extended Data Fig. 1 ). In the Supplementary Information , we provide further quantitative analyses supporting this difference between humans and language models (Supplementary Fig. 7 ).

Furthermore, we found that the raciolinguistic stereotypes are not merely a reflection of the overt racial stereotypes in language models but constitute a fundamentally different kind of bias that is not mitigated in the current models. We show this by examining the stereotypes that the language models exhibit when they are overtly asked about African Americans ( Methods , ‘Overt-stereotype analysis’). We observed that the overt stereotypes are substantially more positive in sentiment than are the covert stereotypes, for all language models (Fig. 2a and Extended Data Fig. 1 ). Strikingly, for RoBERTa, T5, GPT3.5 and GPT4, although their covert stereotypes about speakers of AAE are more negative than the most negative experimentally recorded human stereotypes, their overt stereotypes about African Americans are more positive than the most positive experimentally recorded human stereotypes. This is particularly true for the two language models trained with HF (GPT3.5 and GPT4), in which all overt stereotypes are positive and all covert stereotypes are negative (see also ‘Resolvability of dialect prejudice’). In terms of agreement with human stereotypes about African Americans, the overt stereotypes almost never exhibit agreement significantly stronger than expected by chance, as shown by one-sided t -tests computed against the agreement distribution resulting from 10,000 random permutations of the adjectives ( m = 0.162, s = 0.106; Extended Data Table 2 ). Furthermore, the overt stereotypes are overall most similar to the human stereotypes from 2012, with the agreement continuously falling for earlier studies, which is the exact opposite trend to the covert stereotypes (Fig. 2b ).

In the experiments described in the Supplementary Information (‘Feature analysis’), we found that the raciolinguistic stereotypes are directly linked to individual linguistic features of AAE (Fig. 2c and Supplementary Table 14 ), and that a higher density of such linguistic features results in stronger stereotypical associations (Supplementary Fig. 11 and Supplementary Table 13 ). Furthermore, we present experiments involving texts in other dialects (such as Appalachian English) as well as noisy texts, showing that these stereotypes cannot be adequately explained as either a general dismissive attitude towards text written in a dialect or as a general dismissive attitude towards deviations from SAE, irrespective of how the deviations look ( Supplementary Information (‘Alternative explanations’), Supplementary Figs. 12 and 13 and Supplementary Tables 15 and 16 ). Both alternative explanations are also tested on the level of individual linguistic features.

Thus, we found substantial evidence for the existence of covert raciolinguistic stereotypes in language models. Our experiments show that these stereotypes are similar to the archaic human stereotypes about African Americans that existed before the civil rights movement, are even more negative than the most negative experimentally recorded human stereotypes about African Americans, and are both qualitatively and quantitatively different from the previously reported overt racial stereotypes in language models, indicating that they are a fundamentally different kind of bias. Finally, our analyses demonstrate that the detected stereotypes are inherently linked to AAE and its linguistic features.

Impact of covert racism on AI decisions

To determine what harmful consequences the covert stereotypes have in the real world, we focused on two areas in which racial stereotypes about speakers of AAE and African Americans have been repeatedly shown to bias human decisions: employment and criminality. There is a growing impetus to use AI systems in these areas. Indeed, AI systems are already being used for personnel selection 52 , 53 , including automated analyses of applicants’ social-media posts 54 , 55 , and technologies for predicting legal outcomes are under active development 56 , 57 , 58 . Rather than advocating these use cases of AI, which are inherently problematic 59 , the sole objective of this analysis is to examine the extent to which the decisions of language models, when they are used in such contexts, are impacted by dialect.

First, we examined decisions about employability. Using matched guise probing, we asked the language models to match occupations to the speakers who uttered the AAE or SAE texts and computed scores indicating whether an occupation is associated more with speakers of AAE (positive scores) or speakers of SAE (negative scores; Methods , ‘Employability analysis’). The average score of the occupations was negative ( m = –0.046, s = 0.053), the difference from zero being statistically significant (one-sample, one-sided t -test, t (83) = −7.9, P < 0.001). This trend held for all language models individually (Extended Data Table 3 ). Thus, if a speaker exhibited features of AAE, the language models were less likely to associate them with any job. Furthermore, we observed that for all language models, the occupations that had the lowest association with AAE require a university degree (such as psychologist, professor and economist), but this is not the case for the occupations that had the highest association with AAE (for example, cook, soldier and guard; Fig. 3a ). Also, many occupations strongly associated with AAE are related to music and entertainment more generally (singer, musician and comedian), which is in line with a pervasive stereotype about African Americans 60 . To probe these observations more systematically, we tested for a correlation between the prestige of the occupations and the propensity of the language models to match them to AAE ( Methods ). Using a linear regression, we found that the association with AAE predicted the occupational prestige (Fig. 3b ; β = −7.8, R 2 = 0.193, F (1, 63) = 15.1, P < 0.001). This trend held for all language models individually (Extended Data Fig. 2 and Extended Data Table 4 ), albeit in a less pronounced way for GPT3.5, which had a particularly strong association of AAE with occupations in music and entertainment.

a , Association of different occupations with AAE or SAE. Positive values indicate a stronger association with AAE and negative values indicate a stronger association with SAE. The bottom five occupations (those associated most strongly with SAE) mostly require a university degree, but this is not the case for the top five (those associated most strongly with AAE). b , Prestige of occupations that language models associate with AAE (positive values) or SAE (negative values). The shaded area shows a 95% confidence band around the regression line. The association with AAE or SAE predicts the occupational prestige. Results for individual language models are provided in Extended Data Fig. 2 . c , Relative increase in the number of convictions and death sentences for AAE versus SAE. Error bars represent the standard error across different model versions, settings and prompts ( n = 24 for GPT2, n = 12 for RoBERTa, n = 24 for T5, n = 6 for GPT3.5 and n = 6 for GPT4). In cases of small sample size ( n ≤ 10 for GPT3.5 and GPT4), we plotted the individual results as overlaid dots. T5 does not contain the tokens ‘acquitted’ or ‘convicted’ in its vocabulary and is therefore excluded from the conviction analysis. Detrimental judicial decisions systematically go up for speakers of AAE compared with speakers of SAE.

We then examined decisions about criminality. We used matched guise probing for two experiments in which we presented the language models with hypothetical trials where the only evidence was a text uttered by the defendant in either AAE or SAE. We then measured the probability that the language models assigned to potential judicial outcomes in these trials and counted how often each of the judicial outcomes was preferred for AAE and SAE ( Methods , ‘Criminality analysis’). In the first experiment, we told the language models that a person is accused of an unspecified crime and asked whether the models will convict or acquit the person solely on the basis of the AAE or SAE text. Overall, we found that the rate of convictions was greater for AAE ( r = 68.7%) than SAE ( r = 62.1%; Fig. 3c , left). A chi-squared test found a strong effect ( χ 2 (1, N = 96) = 184.7, P < 0.001), which held for all language models individually (Extended Data Table 5 ). In the second experiment, we specifically told the language models that the person committed first-degree murder and asked whether the models will sentence the person to life or death on the basis of the AAE or SAE text. The overall rate of death sentences was greater for AAE ( r = 27.7%) than for SAE ( r = 22.8%; Fig. 3c , right). A chi-squared test found a strong effect ( χ 2 (1, N = 144) = 425.4, P < 0.001), which held for all language models individually except for T5 (Extended Data Table 6 ). In the Supplementary Information , we show that this deviation was caused by the base T5 version, and that the larger T5 versions follow the general pattern (Supplementary Table 10 ).

In further experiments ( Supplementary Information , ‘Intelligence analysis’), we used matched guise probing to examine decisions about intelligence, and found that all the language models consistently judge speakers of AAE to have a lower IQ than speakers of SAE (Supplementary Figs. 14 and 15 and Supplementary Tables 17 – 19 ).

Resolvability of dialect prejudice

We wanted to know whether the dialect prejudice we observed is resolved by current practices of bias mitigation, such as increasing the size of the language model or including HF in training. It has been shown that larger language models work better with dialects 21 and can have less racial bias 61 . Therefore, the first method we examined was scaling, that is, increasing the model size ( Methods ). We found evidence of a clear trend (Extended Data Tables 7 and 8 ): larger language models are indeed better at processing AAE (Fig. 4a , left), but they are not less prejudiced against speakers of it. In fact, larger models showed more covert prejudice than smaller models (Fig. 4a , right). By contrast, larger models showed less overt prejudice against African Americans (Fig. 4a , right). Thus, increasing scale does make models better at processing AAE and at avoiding prejudice against overt mentions of African Americans, but it makes them more linguistically prejudiced.

a , Language modelling perplexity and stereotype strength on AAE text as a function of model size. Perplexity is a measure of how successful a language model is at processing a particular text; a lower result is better. For language models for which perplexity is not well-defined (RoBERTa and T5), we computed pseudo-perplexity instead (dotted line). Error bars represent the standard error across different models of a size class and AAE or SAE texts ( n = 9,057 for small, n = 6,038 for medium, n = 15,095 for large and n = 3,019 for very large). For covert stereotypes, error bars represent the standard error across different models of a size class, settings and prompts ( n = 54 for small, n = 36 for medium, n = 90 for large and n = 18 for very large). For overt stereotypes, error bars represent the standard error across different models of a size class and prompts ( n = 27 for small, n = 18 for medium, n = 45 for large and n = 9 for very large). Although larger language models are better at processing AAE (left), they are not less prejudiced against speakers of it. Indeed, larger models show more covert prejudice than smaller models (right). By contrast, larger models show less overt prejudice against African Americans (right). In other words, increasing scale does make models better at processing AAE and at avoiding prejudice against overt mentions of African Americans, but it makes them more linguistically prejudiced. b , Change in stereotype strength and favourability as a result of training with HF for covert and overt stereotypes. Error bars represent the standard error across different prompts ( n = 9). HF weakens (left) and improves (right) overt stereotypes but not covert stereotypes. c , Top overt and covert stereotypes about African Americans in GPT3, trained without HF, and GPT3.5, trained with HF. Colour coding as positive (green) and negative (red) is based on ref. 34 . The overt stereotypes get substantially more positive as a result of HF training in GPT3.5, but there is no visible change in favourability for the covert stereotypes.

As a second potential way to resolve dialect prejudice in language models, we examined training with HF 49 , 62 . Specifically, we compared GPT3.5 (ref. 49 ) with GPT3 (ref. 63 ), its predecessor that was trained without using HF ( Methods ). Looking at the top adjectives associated overtly and covertly with African Americans by the two language models, we found that HF resulted in more-positive overt associations but had no clear qualitative effect on the covert associations (Fig. 4c ). This observation was confirmed by quantitative analyses: the inclusion of HF resulted in significantly weaker (no HF, m = 0.135, s = 0.142; HF, m = −0.119, s = 0.234; t (16) = 2.6, P < 0.05) and more favourable (no HF, m = 0.221, s = 0.399; HF, m = 1.047, s = 0.387; t (16) = −6.4, P < 0.001) overt stereotypes but produced no significant difference in the strength (no HF, m = 0.153, s = 0.049; HF, m = 0.187, s = 0.066; t (16) = −1.2, P = 0.3) or unfavourability (no HF, m = −1.146, s = 0.580; HF, m = −1.029, s = 0.196; t (16) = −0.5, P = 0.6) of covert stereotypes (Fig. 4b ). Thus, HF training weakens and ameliorates the overt stereotypes but has no clear effect on the covert stereotypes; in other words, it obscures the racist attitudes on the surface, but more subtle forms of racism, such as dialect prejudice, remain unaffected. This finding is underscored by the fact that the discrepancy between overt and covert stereotypes about African Americans is most pronounced for the two examined language models trained with human feedback (GPT3.5 and GPT4; see ‘Covert stereotypes in language models’). Furthermore, this finding again shows that there is a fundamental difference between overt and covert stereotypes in language models, and that mitigating the overt stereotypes does not automatically translate to mitigated covert stereotypes.

To sum up, neither scaling nor training with HF as applied today resolves the dialect prejudice. The fact that these two methods effectively mitigate racial performance disparities and overt racial stereotypes in language models indicates that this form of covert racism constitutes a different problem that is not addressed by current approaches for improving and aligning language models.

The key finding of this article is that language models maintain a form of covert racial prejudice against African Americans that is triggered by dialect features alone. In our experiments, we avoided overt mentions of race but drew from the racialized meanings of a stigmatized dialect, and could still find historically racist associations with African Americans. The implicit nature of this prejudice, that is, the fact it is about something that is not explicitly expressed in the text, makes it fundamentally different from the overt racial prejudice that has been the focus of previous research. Strikingly, the language models’ covert and overt racial prejudices are often in contradiction with each other, especially for the most recent language models that have been trained with HF (GPT3.5 and GPT4). These two language models obscure the racism, overtly associating African Americans with exclusively positive attributes (such as ‘brilliant’), but our results show that they covertly associate African Americans with exclusively negative attributes (such as ‘lazy’).

We argue that this paradoxical relation between the language models’ covert and overt racial prejudices manifests the inconsistent racial attitudes present in the contemporary society of the United States 8 , 64 . In the Jim Crow era, stereotypes about African Americans were overtly racist, but the normative climate after the civil rights movement made expressing explicitly racist views distasteful. As a result, racism acquired a covert character and continued to exist on a more subtle level. Thus, most white people nowadays report positive attitudes towards African Americans in surveys but perpetuate racial inequalities through their unconscious behaviour, such as their residential choices 65 . It has been shown that negative stereotypes persist, even if they are superficially rejected 66 , 67 . This ambivalence is reflected by the language models we analysed, which are overtly non-racist but covertly exhibit archaic stereotypes about African Americans, showing that they reproduce a colour-blind racist ideology. Crucially, the civil rights movement is generally seen as the period during which racism shifted from overt to covert 68 , 69 , and this is mirrored by our results: all the language models overtly agree the most with human stereotypes from after the civil rights movement, but covertly agree the most with human stereotypes from before the civil rights movement.

Our findings beg the question of how dialect prejudice got into the language models. Language models are pretrained on web-scraped corpora such as WebText 46 , C4 (ref. 48 ) and the Pile 70 , which encode raciolinguistic stereotypes about AAE. A drastic example of this is the use of ‘mock ebonics’ to parodize speakers of AAE 71 . Crucially, a growing body of evidence indicates that language models pick up prejudices present in the pretraining corpus 72 , 73 , 74 , 75 , which would explain how they become prejudiced against speakers of AAE, and why they show varying levels of dialect prejudice as a function of the pretraining corpus. However, the web also abounds with overt racism against African Americans 76 , 77 , so we wondered why the language models exhibit much less overt than covert racial prejudice. We argue that the reason for this is that the existence of overt racism is generally known to people 32 , which is not the case for covert racism 69 . Crucially, this also holds for the field of AI. The typical pipeline of training language models includes steps such as data filtering 48 and, more recently, HF training 62 that remove overt racial prejudice. As a result, much of the overt racism on the web does not end up in the language models. However, there are currently no measures in place to curtail covert racial prejudice when training language models. For example, common datasets for HF training 62 , 78 do not include examples that would train the language models to treat speakers of AAE and SAE equally. As a result, the covert racism encoded in the training data can make its way into the language models in an unhindered fashion. It is worth mentioning that the lack of awareness of covert racism also manifests during evaluation, where it is common to test language models for overt racism but not for covert racism 21 , 63 , 79 , 80 .

As well as the representational harms, by which we mean the pernicious representation of AAE speakers, we also found evidence for substantial allocational harms. This refers to the inequitable allocation of resources to AAE speakers 81 (Barocas et al., unpublished observations), and adds to known cases of language technology putting speakers of AAE at a disadvantage by performing worse on AAE 82 , 83 , 84 , 85 , 86 , 87 , 88 , misclassifying AAE as hate speech 81 , 89 , 90 , 91 or treating AAE as incorrect English 83 , 85 , 92 . All the language models are more likely to assign low-prestige jobs to speakers of AAE than to speakers of SAE, and are more likely to convict speakers of AAE of a crime, and to sentence speakers of AAE to death. Although the details of our tasks are constructed, the findings reveal real and urgent concerns because business and jurisdiction are areas for which AI systems involving language models are currently being developed or deployed. As a consequence, the dialect prejudice we uncovered might already be affecting AI decisions today, for example when a language model is used in application-screening systems to process background information, which might include social-media text. Worryingly, we also observe that larger language models and language models trained with HF exhibit stronger covert, but weaker overt, prejudice. Against the backdrop of continually growing language models and the increasingly widespread adoption of HF training, this has two risks: first, that language models, unbeknownst to developers and users, reach ever-increasing levels of covert prejudice; and second, that developers and users mistake ever-decreasing levels of overt prejudice (the only kind of prejudice currently tested for) for a sign that racism in language models has been solved. There is therefore a realistic possibility that the allocational harms caused by dialect prejudice in language models will increase further in the future, perpetuating the racial discrimination experienced by generations of African Americans.

Matched guise probing examines how strongly a language model associates certain tokens, such as personality traits, with AAE compared with SAE. AAE can be viewed as the treatment condition, whereas SAE functions as the control condition. We start by explaining the basic experimental unit of matched guise probing: measuring how a language model associates certain tokens with an individual text in AAE or SAE. Based on this, we introduce two different settings for matched guise probing (meaning-matched and non-meaning-matched), which are both inspired by the matched guise technique used in sociolinguistics 36 , 37 , 93 , 94 and provide complementary views on the attitudes a language model has about a dialect.

The basic experimental unit of matched guise probing is as follows. Let θ be a language model, t be a text in AAE or SAE, and x be a token of interest, typically a personality trait such as ‘intelligent’. We embed the text in a prompt v , for example v ( t ) = ‘a person who says t tends to be’, and compute P ( x ∣ v ( t ); θ ), which is the probability that θ assigns to x after processing v ( t ). We calculate P ( x ∣ v ( t ); θ ) for equally sized sets T a of AAE texts and T s of SAE texts, comparing various tokens from a set X as possible continuations. It has been shown that P ( x ∣ v ( t ); θ ) can be affected by the precise wording of v , so small modifications of v can have an unpredictable effect on the predictions made by the language model 21 , 95 , 96 . To account for this fact, we consider a set V containing several prompts ( Supplementary Information ). For all experiments, we have provided detailed analyses of variation across prompts in the Supplementary Information .

We conducted matched guise probing in two settings. In the first setting, the texts in T a and T s formed pairs expressing the same underlying meaning, that is, the i -th text in T a (for example, ‘I be so happy when I wake up from a bad dream cus they be feelin too real’) matches the i -th text in T s (for example, ‘I am so happy when I wake up from a bad dream because they feel too real’). For this setting, we used the dataset from ref. 87 , which contains 2,019 AAE tweets together with their SAE translations. In the second setting, the texts in T a and T s did not form pairs, so they were independent texts in AAE and SAE. For this setting, we sampled 2,000 AAE and SAE tweets from the dataset in ref. 83 and used tweets strongly aligned with African Americans for AAE and tweets strongly aligned with white people for SAE ( Supplementary Information (‘Analysis of non-meaning-matched texts’), Supplementary Fig. 1 and Supplementary Table 3 ). In the Supplementary Information , we include examples of AAE and SAE texts for both settings (Supplementary Tables 1 and 2 ). Tweets are well suited for matched guise probing because they are a rich source of dialectal variation 97 , 98 , 99 , especially for AAE 100 , 101 , 102 , but matched guise probing can be applied to any type of text. Although we do not consider it here, matched guise probing can in principle also be applied to speech-based models, with the potential advantage that dialectal variation on the phonetic level could be captured more directly, which would make it possible to study dialect prejudice specific to regional variants of AAE 23 . However, note that a great deal of phonetic variation is reflected orthographically in social-media texts 101 .

It is important to analyse both meaning-matched and non-meaning-matched settings because they capture different aspects of the attitudes a language model has about speakers of AAE. Controlling for the underlying meaning makes it possible to uncover differences in the attitudes of the language model that are solely due to grammatical and lexical features of AAE. However, it is known that various properties other than linguistic features correlate with dialect, such as topics 45 , and these might also influence the attitudes of the language model. Sidelining such properties bears the risk of underestimating the harms that dialect prejudice causes for speakers of AAE in the real world. For example, in a scenario in which a language model is used in the context of automated personnel selection to screen applicants’ social-media posts, the texts of two competing applicants typically differ in content and do not come in pairs expressing the same meaning. The relative advantages of using meaning-matched or non-meaning-matched data for matched guise probing are conceptually similar to the relative advantages of using the same or different speakers for the matched guise technique: more control in the former versus more naturalness in the latter setting 93 , 94 . Because the results obtained in both settings were consistent overall for all experiments, we aggregated them in the main article, but we analysed differences in detail in the Supplementary Information .

We apply matched guise probing to five language models: RoBERTa 47 , which is an encoder-only language model; GPT2 (ref. 46 ), GPT3.5 (ref. 49 ) and GPT4 (ref. 50 ), which are decoder-only language models; and T5 (ref. 48 ), which is an encoder–decoder language model. For each language model, we examined one or more model versions: GPT2 (base), GPT2 (medium), GPT2 (large), GPT2 (xl), RoBERTa (base), RoBERTa (large), T5 (small), T5 (base), T5 (large), T5 (3b), GPT3.5 (text-davinci-003) and GPT4 (0613). Where we used several model versions per language model (GPT2, RoBERTa and T5), the model versions all had the same architecture and were trained on the same data but differed in their size. Furthermore, we note that GPT3.5 and GPT4 are the only language models examined in this paper that were trained with HF, specifically reinforcement learning from human feedback 103 . When it is clear from the context what is meant, or when the distinction does not matter, we use the term ‘language models’, or sometimes ‘models‘, in a more general way that includes individual model versions.

Regarding matched guise probing, the exact method for computing P ( x ∣ v ( t ); θ ) varies across language models and is detailed in the Supplementary Information . For GPT4, for which computing P ( x ∣ v ( t ); θ ) for all tokens of interest was often not possible owing to restrictions imposed by the OpenAI application programming interface (API), we used a slightly modified method for some of the experiments, and this is also discussed in the Supplementary Information . Similarly, some of the experiments could not be done for all language models because of model-specific constraints, which we highlight below. We note that there was at most one language model per experiment for which this was the case.

Covert-stereotype analysis

In the covert-stereotype analysis, the tokens x whose probabilities are measured for matched guise probing are trait adjectives from the Princeton Trilogy 29 , 30 , 31 , 34 , such as ‘aggressive’, ‘intelligent’ and ‘quiet’. We provide details about these adjectives in the Supplementary Information . In the Princeton Trilogy, the adjectives are provided to participants in the form of a list, and participants are asked to select from the list the five adjectives that best characterize a given ethnic group, such as African Americans. The studies that we compare in this paper, which are the original Princeton Trilogy studies 29 , 30 , 31 and a more recent reinstallment 34 , all follow this general set-up and observe a gradual improvement of the expressed stereotypes about African Americans over time, but the exact interpretation of this finding is disputed 32 . Here, we used the adjectives from the Princeton Trilogy in the context of matched guise probing.

Specifically, we first computed P ( x ∣ v ( t ); θ ) for all adjectives, for both the AAE texts and the SAE texts. The method for aggregating the probabilities P ( x ∣ v ( t ); θ ) into association scores between an adjective x and AAE varies for the two settings of matched guise probing. Let ${t}_{{\rm{a}}}^{i}$ be the i -th AAE text in T a and ${t}_{{\rm{s}}}^{i}$ be the i -th SAE text in T s . In the meaning-matched setting, in which ${t}_{{\rm{a}}}^{i}$ and ${t}_{{\rm{s}}}^{i}$ express the same meaning, we computed the prompt-level association score for an adjective x as

where n = ∣ T a ∣ = ∣ T s ∣ . Thus, we measure for each pair of AAE and SAE texts the log ratio of the probability assigned to x following the AAE text and the probability assigned to x following the SAE text, and then average the log ratios of the probabilities across all pairs. In the non-meaning-matched setting, we computed the prompt-level association score for an adjective x as

where again n = ∣ T a ∣ = ∣ T s ∣ . In other words, we first compute the average probability assigned to a certain adjective x following all AAE texts and the average probability assigned to x following all SAE texts, and then measure the log ratio of these average probabilities. The interpretation of q ( x ; v , θ ) is identical in both settings; q ( x ; v , θ ) > 0 means that for a certain prompt v , the language model θ associates the adjective x more strongly with AAE than with SAE, and q ( x ; v , θ ) < 0 means that for a certain prompt v , the language model θ associates the adjective x more strongly with SAE than with AAE. In the Supplementary Information (‘Calibration’), we show that q ( x ; v , θ ) is calibrated 104 , meaning that it does not depend on the prior probability that θ assigns to x in a neutral context.

The prompt-level association scores q ( x ; v , θ ) are the basis for further analyses. We start by averaging q ( x ; v , θ ) across model versions, prompts and settings, and this allows us to rank all adjectives according to their overall association with AAE for individual language models (Fig. 2a ). In this and the following adjective analyses, we focus on the five adjectives that exhibit the highest association with AAE, making it possible to consistently compare the language models with the results from the Princeton Trilogy studies, most of which do not report the full ranking of all adjectives. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Fig. 2 and Supplementary Table 4 ).

Next, we wanted to measure the agreement between language models and humans through time. To do so, we considered the five adjectives most strongly associated with African Americans for each study and evaluated how highly these adjectives are ranked by the language models. Specifically, let R l = [ x 1 , …, x ∣ X ∣ ] be the adjective ranking generated by a language model and ${R}_{h}^{5}$ = [ x 1 , …, x 5 ] be the ranking of the top five adjectives generated by the human participants in one of the Princeton Trilogy studies. A typical measure to evaluate how highly the adjectives from ${R}_{h}^{5}$ are ranked within R l is average precision, AP 51 . However, AP does not take the internal ranking of the adjectives in ${R}_{h}^{5}$ into account, which is not ideal for our purposes; for example, AP does not distinguish whether the top-ranked adjective for humans is on the first or on the fifth rank for a language model. To remedy this, we computed the mean average precision, MAP, for different subsets of ${R}_{h}^{5}$ ,

where ${R}_{h}^{i}$ denotes the top i adjectives from the human ranking. MAP = 1 if, and only if, the top five adjectives from ${R}_{h}^{5}$ have an exact one-to-one correspondence with the top five adjectives from R l , so, unlike AP, it takes the internal ranking of the adjectives into account. We computed an individual agreement score for each language model and prompt, so we average the q ( x ; v , θ ) association scores for all model versions of a language model (GPT2, for example) and the two settings (meaning-matched and non-meaning-matched) to generate R l . Because the OpenAI API for GPT4 does not give access to the probabilities for all adjectives, we excluded GPT4 from this analysis. Results are presented in Fig. 2b and Extended Data Table 1 . In the Supplementary Information (‘Agreement analysis’), we analyse variation across model versions, settings and prompts (Supplementary Figs. 3 – 5 ).

To analyse the favourability of the stereotypes about African Americans, we drew from crowd-sourced favourability ratings collected previously 34 for the adjectives from the Princeton Trilogy that range between −2 (‘very unfavourable’, meaning very negative) and 2 (‘very favourable’, meaning very positive). For example, the favourability rating of ‘cruel’ is −1.81 and the favourability rating of ‘brilliant’ is 1.86. We computed the average favourability of the top five adjectives, weighting the favourability ratings of individual adjectives by their association scores with AAE and African Americans. More formally, let R 5 = [ x 1 , …, x 5 ] be the ranking of the top five adjectives generated by either a language model or humans. Furthermore, let f ( x ) be the favourability rating of adjective x as reported in ref. 34 , and let q ( x ) be the overall association score of adjective x with AAE or African Americans that is used to generate R 5 . For the Princeton Trilogy studies, q ( x ) is the percentage of participants who have assigned x to African Americans. For language models, q ( x ) is the average value of q ( x ; v , θ ). We then computed the weighted average favourability, F , of the top five adjectives as

As a result of the weighting, the top-ranked adjective contributed more to the average than the second-ranked adjective, and so on. Results are presented in Extended Data Fig. 1 . To check for consistency, we also computed the average favourability of the top five adjectives without weighting, which yields similar results (Supplementary Fig. 6) .

Overt-stereotype analysis

The overt-stereotype analysis closely followed the methodology of the covert-stereotype analysis, with the difference being that instead of providing the language models with AAE and SAE texts, we provided them with overt descriptions of race (specifically, ‘Black’/‘black’ and ‘White’/‘white’). This methodological difference is also reflected by a different set of prompts ( Supplementary Information ). As a result, the experimental set-up is very similar to existing studies on overt racial bias in language models 4 , 7 . All other aspects of the analysis (such as computing adjective association scores) were identical to the analysis for covert stereotypes. This also holds for GPT4, for which we again could not conduct the agreement analysis.

We again present average results for the five language models in the main article. Results broken down for individual model versions are provided in the Supplementary Information , where we also analyse variation across prompts (Supplementary Fig. 8 and Supplementary Table 5 ).

Employability analysis

The general set-up of the employability analysis was identical to the stereotype analyses: we fed text written in either AAE or SAE, embedded in prompts, into the language models and analysed the probabilities that they assigned to different continuation tokens. However, instead of trait adjectives, we considered occupations for X and also used a different set of prompts ( Supplementary Information ). We created a list of occupations, drawing from previously published lists 6 , 76 , 105 , 106 , 107 . We provided details about these occupations in the Supplementary Information . We then computed association scores q ( x ; v , θ ) between individual occupations x and AAE, following the same methodology as for computing adjective association scores, and ranked the occupations according to q ( x ; v , θ ) for the language models. To probe the prestige associated with the occupations, we drew from a dataset of occupational prestige 105 that is based on the 2012 US General Social Survey and measures prestige on a scale from 1 (low prestige) to 9 (high prestige). For GPT4, we could not conduct the parts of the analysis that require scores for all occupations.

We again present average results for the five language models in the main article. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Tables 6 – 8 ).

Criminality analysis

The set-up of the criminality analysis is different from the previous experiments in that we did not compute aggregate association scores between certain tokens (such as trait adjectives) and AAE but instead asked the language models to make discrete decisions for each AAE and SAE text. More specifically, we simulated trials in which the language models were prompted to use AAE or SAE texts as evidence to make a judicial decision. We then aggregated the judicial decisions into summary statistics.

We conducted two experiments. In the first experiment, the language models were asked to determine whether a person accused of committing an unspecified crime should be acquitted or convicted. The only evidence provided to the language models was a statement made by the defendant, which was an AAE or SAE text. In the second experiment, the language models were asked to determine whether a person who committed first-degree murder should be sentenced to life or death. Similarly to the first (general conviction) experiment, the only evidence provided to the language models was a statement made by the defendant, which was an AAE or SAE text. Note that the AAE and SAE texts were the same texts as in the other experiments and did not come from a judicial context. Rather than testing how well language models could perform the tasks of predicting acquittal or conviction and life penalty or death penalty (an application of AI that we do not support), we were interested to see to what extent the decisions of the language models, made in the absence of any real evidence, were impacted by dialect. Although providing the language models with extra evidence as well as the AAE and SAE texts would have made the experiments more similar to real trials, it would have confounded the effect that dialect has on its own (the key effect of interest), so we did not consider this alternative set-up here. We focused on convictions and death penalties specifically because these are the two areas of the criminal justice system for which racial disparities have been described in the most robust and indisputable way: African Americans represent about 12% of the adult population of the United States, but they represent 33% of inmates 108 and more than 41% of people on death row 109 .

Methodologically, we used prompts that asked the language models to make a judicial decision ( Supplementary Information ). For a specific text, t , which is in AAE or SAE, we computed p ( x ∣ v ( t ); θ ) for the tokens x that correspond to the judicial outcomes of interest (‘acquitted’ or ‘convicted’, and ‘life’ or ‘death’). T5 does not contain the tokens ‘acquitted’ and ‘convicted’ in its vocabulary, so is was excluded from the conviction analysis. Because the language models might assign different prior probabilities to the outcome tokens, we calibrated them using their probabilities in a neutral context following v , meaning without text t 104 . Whichever outcome had the higher calibrated probability was counted as the decision. We aggregated the detrimental decisions (convictions and death penalties) and compared their rates (percentages) between AAE and SAE texts. An alternative approach would have been to generate the judicial decision by sampling from the language models, which would have allowed us to induce the language models to generate justifications of their decisions. However, this approach has three disadvantages: first, encoder-only language models such as RoBERTa do not lend themselves to text generation; second, it would have been necessary to apply jail-breaking for some of the language models, which can have unpredictable effects, especially in the context of socially sensitive tasks; and third, model-generated justifications are frequently not aligned with actual model behaviours 110 .

We again present average results on the level of language models in the main article. Results for individual model versions are provided in the Supplementary Information , where we also analyse variation across settings and prompts (Supplementary Figs. 9 and 10 and Supplementary Tables 9 – 12 ).

Scaling analysis

In the scaling analysis, we examined whether increasing the model size alleviated the dialect prejudice. Because the content of the covert stereotypes is quite consistent and does not vary substantially between models with different sizes, we instead analysed the strength with which the language models maintain these stereotypes. We split the model versions of all language models into four groups according to their size using the thresholds of 1.5 × 10 8 , 3.5 × 10 8 and 1.0 × 10 10 (Extended Data Table 7 ).

To evaluate the familiarity of the models with AAE, we measured their perplexity on the datasets used for the two evaluation settings 83 , 87 . Perplexity is defined as the exponentiated average negative log-likelihood of a sequence of tokens 111 , with lower values indicating higher familiarity. Perplexity requires the language models to assign probabilities to full sequences of tokens, which is only the case for GPT2 and GPT3.5. For RoBERTa and T5, we resorted to pseudo-perplexity 112 as the measure of familiarity. Results are only comparable across language models with the same familiarity measure. We excluded GPT4 from this analysis because it is not possible to compute perplexity using the OpenAI API.

To evaluate the stereotype strength, we focused on the stereotypes about African Americans reported in ref. 29 , which the language models’ covert stereotypes agree with most strongly. We split the set of adjectives X into two subsets: the set of stereotypical adjectives in ref. 29 , X s , and the set of non-stereotypical adjectives, X n = X \ X s . For each model with a specific size, we then computed the average value of q ( x ; v , θ ) for all adjectives in X s , which we denote as q s ( θ ), and the average value of q ( x ; v , θ ) for all adjectives in X n , which we denote as q n ( θ ). The stereotype strength of a model θ , or more specifically the strength of the stereotypes about African Americans reported in ref. 29 , can then be computed as

A positive value of δ ( θ ) means that the model associates the stereotypical adjectives in X s more strongly with AAE than the non-stereotypical adjectives in X n , whereas a negative value of δ ( θ ) indicates anti-stereotypical associations, meaning that the model associates the non-stereotypical adjectives in X n more strongly with AAE than the stereotypical adjectives in X s . For the overt stereotypes, we used the same split of adjectives into X s and X n because we wanted to directly compare the strength with which models of a certain size endorse the stereotypes overtly as opposed to covertly. All other aspects of the experimental set-up are identical to the main analyses of covert and overt stereotypes.

HF analysis

We compared GPT3.5 (ref. 49 ; text-davinci-003) with GPT3 (ref. 63 ; davinci), its predecessor language model that was trained without HF. Similarly to other studies that compare these two language models 113 , this set-up allowed us to examine the effects of HF training as done for GPT3.5 in isolation. We compared the two language models in terms of favourability and stereotype strength. For favourability, we followed the methodology we used for the overt-stereotype analysis and evaluated the average weighted favourability of the top five adjectives associated with AAE. For stereotype strength, we followed the methodology we used for the scaling analysis and evaluated the average strength of the stereotypes as reported in ref. 29 .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All the datasets used in this study are publicly available. The dataset released as ref. 87 can be found at https://aclanthology.org/2020.emnlp-main.473/ . The dataset released as ref. 83 can be found at http://slanglab.cs.umass.edu/TwitterAAE/ . The human stereotype scores used for evaluation can be found in the published articles of the Princeton Trilogy studies 29 , 30 , 31 , 34 . The most recent of these articles 34 also contains the human favourability scores for the trait adjectives. The dataset of occupational prestige that we used for the employability analysis can be found in the corresponding paper 105 . The Brown Corpus 114 , which we used for the Supplementary Information (‘Feature analysis’), can be found at http://www.nltk.org/nltk_data/ . The dataset containing the parallel AAE, Appalachian English and Indian English texts 115 , which we used in the Supplementary Information (‘Alternative explanations’), can be found at https://huggingface.co/collections/SALT-NLP/value-nlp-666b60a7f76c14551bda4f52 .

Code availability

Our code is written in Python and draws on the Python packages openai and transformers for language-model probing, as well as numpy, pandas, scipy and statsmodels for data analysis. The feature analysis described in the Supplementary Information also uses the VALUE Python library 88 . Our code is publicly available on GitHub at https://github.com/valentinhofmann/dialect-prejudice .

Zhao, W. et al. WildChat: 1M ChatGPT interaction logs in the wild. In Proc. Twelfth International Conference on Learning Representations (OpenReview.net, 2024).

Zheng, L. et al. LMSYS-Chat-1M: a large-scale real-world LLM conversation dataset. In Proc. Twelfth International Conference on Learning Representations (OpenReview.net, 2024).

Gaebler, J. D., Goel, S., Huq, A. & Tambe, P. Auditing the use of language models to guide hiring decisions. Preprint at https://arxiv.org/abs/2404.03086 (2024).

Sheng, E., Chang, K.-W., Natarajan, P. & Peng, N. The woman worked as a babysitter: on biases in language generation. In Proc. 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing (eds Inui. K. et al.) 3407–3412 (Association for Computational Linguistics, 2019).

Nangia, N., Vania, C., Bhalerao, R. & Bowman, S. R. CrowS-Pairs: a challenge dataset for measuring social biases in masked language models. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing (eds Webber, B. et al.) 1953–1967 (Association for Computational Linguistics, 2020).

Nadeem, M., Bethke, A. & Reddy, S. StereoSet: measuring stereotypical bias in pretrained language models. In Proc. 59th Annual Meeting of the Association for Computational Linguistics and 11th International Joint Conference on Natural Language Processing (eds Zong, C. et al.) 5356–5371 (Association for Computational Linguistics, 2021).

Cheng, M., Durmus, E. & Jurafsky, D. Marked personas: using natural language prompts to measure stereotypes in language models. In Proc. 61st Annual Meeting of the Association for Computational Linguistics (eds Rogers, A. et al.) 1504–1532 (Association for Computational Linguistics, 2023).

Bonilla-Silva, E. Racism without Racists: Color-Blind Racism and the Persistence of Racial Inequality in America 4th edn (Rowman & Littlefield, 2014).

Golash-Boza, T. A critical and comprehensive sociological theory of race and racism. Sociol. Race Ethn. 2 , 129–141 (2016).

Article Google Scholar

Kasneci, E. et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 103 , 102274 (2023).

Nay, J. J. et al. Large language models as tax attorneys: a case study in legal capabilities emergence. Philos. Trans. R. Soc. A 382 , 20230159 (2024).

Article ADS Google Scholar

Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature 619 , 357–362 (2023).

Article ADS CAS PubMed PubMed Central Google Scholar

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V. & Kalai, A. Man is to computer programmer as woman is to homemaker? Debiasing word embeddings. Adv. Neural Inf. Process. Syst. 30 , 4356–4364 (2016).

Google Scholar

Caliskan, A., Bryson, J. J. & Narayanan, A. Semantics derived automatically from language corpora contain human-like biases. Science 356 , 183–186 (2017).

Article ADS CAS PubMed Google Scholar

Basta, C., Costa-jussà, M. R. & Casas, N. Evaluating the underlying gender bias in contextualized word embeddings. In Proc. First Workshop on Gender Bias in Natural Language Processing (eds Costa-jussà, M. R. et al.) 33–39 (Association for Computational Linguistics, 2019).

Kurita, K., Vyas, N., Pareek, A., Black, A. W. & Tsvetkov, Y. Measuring bias in contextualized word representations. In Proc. First Workshop on Gender Bias in Natural Language Processing (eds Costa-jussà, M. R. et al.) 166–172 (Association for Computational Linguistics, 2019).

Abid, A., Farooqi, M. & Zou, J. Persistent anti-muslim bias in large language models. In Proc. 2021 AAAI/ACM Conference on AI, Ethics, and Society (eds Fourcade, M. et al.) 298–306 (Association for Computing Machinery, 2021).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? In Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (Association for Computing Machinery, 2021).

Li, L. & Bamman, D. Gender and representation bias in GPT-3 generated stories. In Proc. Third Workshop on Narrative Understanding (eds Akoury, N. et al.) 48–55 (Association for Computational Linguistics, 2021).

Tamkin, A. et al. Evaluating and mitigating discrimination in language model decisions. Preprint at https://arxiv.org/abs/2312.03689 (2023).

Rae, J. W. et al. Scaling language models: methods, analysis & insights from training Gopher. Preprint at https://arxiv.org/abs/2112.11446 (2021).

Green, L. J. African American English: A Linguistic Introduction (Cambridge Univ. Press, 2002).

King, S. From African American Vernacular English to African American Language: rethinking the study of race and language in African Americans’ speech. Annu. Rev. Linguist. 6 , 285–300 (2020).

Purnell, T., Idsardi, W. & Baugh, J. Perceptual and phonetic experiments on American English dialect identification. J. Lang. Soc. Psychol. 18 , 10–30 (1999).

Massey, D. S. & Lundy, G. Use of Black English and racial discrimination in urban housing markets: new methods and findings. Urban Aff. Rev. 36 , 452–469 (2001).

Dunbar, A., King, S. & Vaughn, C. Dialect on trial: an experimental examination of raciolinguistic ideologies and character judgments. Race Justice https://doi.org/10.1177/21533687241258772 (2024).

Rickford, J. R. & King, S. Language and linguistics on trial: Hearing Rachel Jeantel (and other vernacular speakers) in the courtroom and beyond. Language 92 , 948–988 (2016).

Grogger, J. Speech patterns and racial wage inequality. J. Hum. Resour. 46 , 1–25 (2011).

Katz, D. & Braly, K. Racial stereotypes of one hundred college students. J. Abnorm. Soc. Psychol. 28 , 280–290 (1933).

Gilbert, G. M. Stereotype persistance and change among college students. J. Abnorm. Soc. Psychol. 46 , 245–254 (1951).

Article CAS Google Scholar

Karlins, M., Coffman, T. L. & Walters, G. On the fading of social stereotypes: studies in three generations of college students. J. Pers. Soc. Psychol. 13 , 1–16 (1969).

Article CAS PubMed Google Scholar

Devine, P. G. & Elliot, A. J. Are racial stereotypes really fading? The Princeton Trilogy revisited. Pers. Soc. Psychol. Bull. 21 , 1139–1150 (1995).

Madon, S. et al. Ethnic and national stereotypes: the Princeton Trilogy revisited and revised. Pers. Soc. Psychol. Bull. 27 , 996–1010 (2001).

Bergsieker, H. B., Leslie, L. M., Constantine, V. S. & Fiske, S. T. Stereotyping by omission: eliminate the negative, accentuate the positive. J. Pers. Soc. Psychol. 102 , 1214–1238 (2012).

Article PubMed PubMed Central Google Scholar

Ghavami, N. & Peplau, L. A. An intersectional analysis of gender and ethnic stereotypes: testing three hypotheses. Psychol. Women Q. 37 , 113–127 (2013).

Lambert, W. E., Hodgson, R. C., Gardner, R. C. & Fillenbaum, S. Evaluational reactions to spoken languages. J. Abnorm. Soc. Psychol. 60 , 44–51 (1960).

Ball, P. Stereotypes of Anglo-Saxon and non-Anglo-Saxon accents: some exploratory Australian studies with the matched guise technique. Lang. Sci. 5 , 163–183 (1983).

Thomas, E. R. & Reaser, J. Delimiting perceptual cues used for the ethnic labeling of African American and European American voices. J. Socioling. 8 , 54–87 (2004).

Atkins, C. P. Do employment recruiters discriminate on the basis of nonstandard dialect? J. Employ. Couns. 30 , 108–118 (1993).

Payne, K., Downing, J. & Fleming, J. C. Speaking Ebonics in a professional context: the role of ethos/source credibility and perceived sociability of the speaker. J. Tech. Writ. Commun. 30 , 367–383 (2000).

Rodriguez, J. I., Cargile, A. C. & Rich, M. D. Reactions to African-American vernacular English: do more phonological features matter? West. J. Black Stud. 28 , 407–414 (2004).

Billings, A. C. Beyond the Ebonics debate: attitudes about Black and standard American English. J. Black Stud. 36 , 68–81 (2005).

Kurinec, C. A. & Weaver, C. III “Sounding Black”: speech stereotypicality activates racial stereotypes and expectations about appearance. Front. Psychol. 12 , 785283 (2021).

Rosa, J. & Flores, N. Unsettling race and language: toward a raciolinguistic perspective. Lang. Soc. 46 , 621–647 (2017).

Salehi, B., Hovy, D., Hovy, E. & Søgaard, A. Huntsville, hospitals, and hockey teams: names can reveal your location. In Proc. 3rd Workshop on Noisy User-generated Text (eds Derczynski, L. et al.) 116–121 (Association for Computational Linguistics, 2017).

Radford, A. et al. Language models are unsupervised multitask learners. OpenAI https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (2019).

Liu, Y. et al. RoBERTa: a robustly optimized BERT pretraining approach. Preprint at https://arxiv.org/abs/1907.11692 (2019).

Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21 , 1–67 (2020).

MathSciNet Google Scholar

Ouyang, L. et al. Training language models to follow instructions with human feedback. In Proc. 36th Conference on Neural Information Processing Systems (eds Koyejo, S. et al.) 27730–27744 (NeurIPS, 2022).

OpenAI et al. GPT-4 technical report. Preprint at https://arxiv.org/abs/2303.08774 (2023).

Zhang, E. & Zhang, Y. Average precision. In Encyclopedia of Database Systems (eds Liu, L. & Özsu, M. T.) 192–193 (Springer, 2009).

Black, J. S. & van Esch, P. AI-enabled recruiting: what is it and how should a manager use it? Bus. Horiz. 63 , 215–226 (2020).

Hunkenschroer, A. L. & Luetge, C. Ethics of AI-enabled recruiting and selection: a review and research agenda. J. Bus. Ethics 178 , 977–1007 (2022).

Upadhyay, A. K. & Khandelwal, K. Applying artificial intelligence: implications for recruitment. Strateg. HR Rev. 17 , 255–258 (2018).

Tippins, N. T., Oswald, F. L. & McPhail, S. M. Scientific, legal, and ethical concerns about AI-based personnel selection tools: a call to action. Pers. Assess. Decis. 7 , 1 (2021).

Aletras, N., Tsarapatsanis, D., Preoţiuc-Pietro, D. & Lampos, V. Predicting judicial decisions of the European Court of Human Rights: a natural language processing perspective. PeerJ Comput. Sci. 2 , e93 (2016).

Surden, H. Artificial intelligence and law: an overview. Ga State Univ. Law Rev. 35 , 1305–1337 (2019).

Medvedeva, M., Vols, M. & Wieling, M. Using machine learning to predict decisions of the European Court of Human Rights. Artif. Intell. Law 28 , 237–266 (2020).

Weidinger, L. et al. Taxonomy of risks posed by language models. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 214–229 (Association for Computing Machinery, 2022).

Czopp, A. M. & Monteith, M. J. Thinking well of African Americans: measuring complimentary stereotypes and negative prejudice. Basic Appl. Soc. Psychol. 28 , 233–250 (2006).

Chowdhery, A. et al. PaLM: scaling language modeling with pathways. J. Mach. Learn. Res. 24 , 11324–11436 (2023).

Bai, Y. et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. Preprint at https://arxiv.org/abs/2204.05862 (2022).

Brown, T. B. et al. Language models are few-shot learners. In Proc. 34th International Conference on Neural Information Processing Systems (eds Larochelle, H. et al.) 1877–1901 (NeurIPS, 2020).

Dovidio, J. F. & Gaertner, S. L. Aversive racism. Adv. Exp. Soc. Psychol. 36 , 1–52 (2004).

Schuman, H., Steeh, C., Bobo, L. D. & Krysan, M. (eds) Racial Attitudes in America: Trends and Interpretations (Harvard Univ. Press, 1998).

Crosby, F., Bromley, S. & Saxe, L. Recent unobtrusive studies of Black and White discrimination and prejudice: a literature review. Psychol. Bull. 87 , 546–563 (1980).

Terkel, S. Race: How Blacks and Whites Think and Feel about the American Obsession (New Press, 1992).

Jackman, M. R. & Muha, M. J. Education and intergroup attitudes: moral enlightenment, superficial democratic commitment, or ideological refinement? Am. Sociol. Rev. 49 , 751–769 (1984).

Bonilla-Silva, E. The New Racism: Racial Structure in the United States, 1960s–1990s. In Race, Ethnicity, and Nationality in the United States: Toward the Twenty-First Century 1st edn (ed. Wong, P.) Ch. 4 (Westview Press, 1999).

Gao, L. et al. The Pile: an 800GB dataset of diverse text for language modeling. Preprint at https://arxiv.org/abs/2101.00027 (2021).

Ronkin, M. & Karn, H. E. Mock Ebonics: linguistic racism in parodies of Ebonics on the internet. J. Socioling. 3 , 360–380 (1999).

Dodge, J. et al. Documenting large webtext corpora: a case study on the Colossal Clean Crawled Corpus. In Proc. 2021 Conference on Empirical Methods in Natural Language Processing (eds Moens, M.-F. et al.) 1286–1305 (Association for Computational Linguistics, 2021).

Steed, R., Panda, S., Kobren, A. & Wick, M. Upstream mitigation is not all you need: testing the bias transfer hypothesis in pre-trained language models. In Proc. 60th Annual Meeting of the Association for Computational Linguistics (eds Muresan, S. et al.) 3524–3542 (Association for Computational Linguistics, 2022).

Feng, S., Park, C. Y., Liu, Y. & Tsvetkov, Y. From pretraining data to language models to downstream tasks: tracking the trails of political biases leading to unfair NLP models. In Proc. 61st Annual Meeting of the Association for Computational Linguistics (eds Rogers, A. et al.) 11737–11762 (Association for Computational Linguistics, 2023).

Köksal, A. et al. Language-agnostic bias detection in language models with bias probing. In Findings of the Association for Computational Linguistics: EMNLP 2023 (eds Bouamor, H. et al.) 12735–12747 (Association for Computational Linguistics, 2023).

Garg, N., Schiebinger, L., Jurafsky, D. & Zou, J. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proc. Natl Acad. Sci. USA 115 , E3635–E3644 (2018).

Ferrer, X., van Nuenen, T., Such, J. M. & Criado, N. Discovering and categorising language biases in Reddit. In Proc. Fifteenth International AAAI Conference on Web and Social Media (eds Budak, C. et al.) 140–151 (Association for the Advancement of Artificial Intelligence, 2021).

Ethayarajh, K., Choi, Y. & Swayamdipta, S. Understanding dataset difficulty with V-usable information. In Proc. 39th International Conference on Machine Learning (eds Chaudhuri, K. et al.) 5988–6008 (Proceedings of Machine Learning Research, 2022).

Hoffmann, J. et al. Training compute-optimal large language models. Preprint at https://arxiv.org/abs/2203.15556 (2022).

Liang, P. et al. Holistic evaluation of language models. Transactions on Machine Learning Research https://openreview.net/forum?id=iO4LZibEqW (2023).

Blodgett, S. L., Barocas, S., Daumé III, H. & Wallach, H. Language (technology) is power: A critical survey of “bias” in NLP. In Proc. 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 5454–5476 (Association for Computational Linguistics, 2020).

Jørgensen, A., Hovy, D. & Søgaard, A. Challenges of studying and processing dialects in social media. In Proc. Workshop on Noisy User-generated Text (eds Xu, W. et al.) 9–18 (Association for Computational Linguistics, 2015).

Blodgett, S. L., Green, L. & O’Connor, B. Demographic dialectal variation in social media: a case study of African-American English. In Proc. 2016 Conference on Empirical Methods in Natural Language Processing (eds Su, J. et al.) 1119–1130 (Association for Computational Linguistics, 2016).

Jørgensen, A., Hovy, D. & Søgaard, A. Learning a POS tagger for AAVE-like language. In Proc. 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Knight, K. et al.) 1115–1120 (Association for Computational Linguistics, 2016).

Blodgett, S. L. & O’Connor, B. Racial disparity in natural language processing: a case study of social media African-American English. Preprint at https://arxiv.org/abs/1707.00061 (2017).

Blodgett, S. L., Wei, J. & O’Connor, B. Twitter universal dependency parsing for African-American and mainstream American English. In Proc. 56th Annual Meeting of the Association for Computational Linguistics (eds Gurevych, I. & Miyao, Y.) 1415–1425 (Association for Computational Linguistics, 2018).

Groenwold, S. et al. Investigating African-American vernacular English in transformer-based text generation. In Proc. 2020 Conference on Empirical Methods in Natural Language Processing (eds Webber, B. et al.) 5877–5883 (Association for Computational Linguistics, 2020).

Ziems, C., Chen, J., Harris, C., Anderson, J. & Yang, D. VALUE: Understanding dialect disparity in NLU. In Proc. 60th Annual Meeting of the Association for Computational Linguistics (eds Muresan, S. et al.) 3701–3720 (Association for Computational Linguistics, 2022).

Davidson, T., Bhattacharya, D. & Weber, I. Racial bias in hate speech and abusive language detection datasets. In Proc. Third Workshop on Abusive Language Online (eds Roberts, S. T. et al.) 25–35 (Association for Computational Linguistics, 2019).

Sap, M., Card, D., Gabriel, S., Choi, Y. & Smith, N. A. The risk of racial bias in hate speech detection. In Proc. 57th Annual Meeting of the Association for Computational Linguistics (eds Korhonen, A. et al.) 1668–1678 (Association for Computational Linguistics, 2019).

Harris, C., Halevy, M., Howard, A., Bruckman, A. & Yang, D. Exploring the role of grammar and word choice in bias toward African American English (AAE) in hate speech classification. In Proc. 2022 ACM Conference on Fairness, Accountability, and Transparency 789–798 (Association for Computing Machinery, 2022).

Gururangan, S. et al. Whose language counts as high quality? Measuring language ideologies in text data selection. In Proc. 2022 Conference on Empirical Methods in Natural Language Processing (eds Goldberg, Y. et al.) 2562–2580 (Association for Computational Linguistics, 2022).

Gaies, S. J. & Beebe, J. D. The matched-guise technique for measuring attitudes and their implications for language education: a critical assessment. In Language Acquisition and the Second/Foreign Language Classroom (ed. Sadtano, E.) 156–178 (SEAMEO Regional Language Centre, 1991).

Hudson, R. A. Sociolinguistics (Cambridge Univ. Press, 1996).

Delobelle, P., Tokpo, E., Calders, T. & Berendt, B. Measuring fairness with biased rulers: a comparative study on bias metrics for pre-trained language models. In Proc. 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Carpuat, M. et al.) 1693–1706 (Association for Computational Linguistics, 2022).

Mattern, J., Jin, Z., Sachan, M., Mihalcea, R. & Schölkopf, B. Understanding stereotypes in language models: Towards robust measurement and zero-shot debiasing. Preprint at https://arxiv.org/abs/2212.10678 (2022).

Eisenstein, J., O’Connor, B., Smith, N. A. & Xing, E. P. A latent variable model for geographic lexical variation. In Proc. 2010 Conference on Empirical Methods in Natural Language Processing (eds Li, H. & Màrquez, L.) 1277–1287 (Association for Computational Linguistics, 2010).

Doyle, G. Mapping dialectal variation by querying social media. In Proc. 14th Conference of the European Chapter of the Association for Computational Linguistics (eds Wintner, S. et al.) 98–106 (Association for Computational Linguistics, 2014).

Huang, Y., Guo, D., Kasakoff, A. & Grieve, J. Understanding U.S. regional linguistic variation with Twitter data analysis. Comput. Environ. Urban Syst. 59 , 244–255 (2016).

Eisenstein, J. What to do about bad language on the internet. In Proc. 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Vanderwende, L. et al.) 359–369 (Association for Computational Linguistics, 2013).

Eisenstein, J. Systematic patterning in phonologically-motivated orthographic variation. J. Socioling. 19 , 161–188 (2015).

Jones, T. Toward a description of African American vernacular English dialect regions using “Black Twitter”. Am. Speech 90 , 403–440 (2015).

Christiano, P. F. et al. Deep reinforcement learning from human preferences. Proc. 31st International Conference on Neural Information Processing Systems (eds von Luxburg, U. et al.) 4302–4310 (NeurIPS, 2017).

Zhao, T. Z., Wallace, E., Feng, S., Klein, D. & Singh, S. Calibrate before use: Improving few-shot performance of language models. In Proc. 38th International Conference on Machine Learning (eds Meila, M. & Zhang, T.) 12697–12706 (Proceedings of Machine Learning Research, 2021).

Smith, T. W. & Son, J. Measuring Occupational Prestige on the 2012 General Social Survey (NORC at Univ. Chicago, 2014).

Zhao, J., Wang, T., Yatskar, M., Ordonez, V. & Chang, K.-W. Gender bias in coreference resolution: evaluation and debiasing methods. In Proc. 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (eds Walker, M. et al.) 15–20 (Association for Computational Linguistics, 2018).

Hughes, B. T., Srivastava, S., Leszko, M. & Condon, D. M. Occupational prestige: the status component of socioeconomic status. Collabra Psychol. 10 , 92882 (2024).

Gramlich, J. The gap between the number of blacks and whites in prison is shrinking. Pew Research Centre https://www.pewresearch.org/short-reads/2019/04/30/shrinking-gap-between-number-of-blacks-and-whites-in-prison (2019).

Walsh, A. The criminal justice system is riddled with racial disparities. Prison Policy Initiative Briefing https://www.prisonpolicy.org/blog/2016/08/15/cjrace (2016).

Röttger, P. et al. Political compass or spinning arrow? Towards more meaningful evaluations for values and opinions in large language models. Preprint at https://arxiv.org/abs/2402.16786 (2024).

Jurafsky, D. & Martin, J. H. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition (Prentice Hall, 2000).

Salazar, J., Liang, D., Nguyen, T. Q. & Kirchhoff, K. Masked language model scoring. In Proc. 58th Annual Meeting of the Association for Computational Linguistics (eds Jurafsky, D. et al.) 2699–2712 (Association for Computational Linguistics, 2020).

Santurkar, S. et al. Whose opinions do language models reflect? In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 29971–30004 (Proceedings of Machine Learning Research, 2023).

Francis, W. N. & Kucera, H. Brown Corpus Manual (Brown Univ.,1979).

Ziems, C. et al. Multi-VALUE: a framework for cross-dialectal English NLP. In Proc. 61st Annual Meeting of the Association for Computational Linguistics (eds Rogers, A. et al.) 744–768 (Association for Computational Linguistics, 2023).

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Acknowledgements

V.H. was funded by the German Academic Scholarship Foundation. P.R.K. was funded in part by the Open Phil AI Fellowship. This work was also funded by the Hoffman-Yee Research Grants programme and the Stanford Institute for Human-Centered Artificial Intelligence. We thank A. Köksal, D. Hovy, K. Gligorić, M. Harrington, M. Casillas, M. Cheng and P. Röttger for feedback on an earlier version of the article.

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V.H., P.R.K., D.J. and S.K. designed the research. V.H. performed the research and analysed the data. V.H., P.R.K., D.J. and S.K. wrote the paper.

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Extended data figures and tables

Extended data fig. 1 weighted average favourability of top stereotypes about african americans in humans and top overt as well as covert stereotypes about african americans in language models (lms)..

The overt stereotypes are more favourable than the reported human stereotypes, except for GPT2. The covert stereotypes are substantially less favourable than the least favourable reported human stereotypes from 1933. Results without weighting, which are very similar, are provided in Supplementary Fig. 6 .

Extended Data Fig. 2 Prestige of occupations associated with AAE (positive values) versus SAE (negative values), for individual language models.

The shaded areas show 95% confidence bands around the regression lines. The association with AAE versus SAE is negatively correlated with occupational prestige, for all language models. We cannot conduct this analysis with GPT4 since the OpenAI API does not give access to the probabilities for all occupations.

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Hofmann, V., Kalluri, P.R., Jurafsky, D. et al. AI generates covertly racist decisions about people based on their dialect. Nature (2024). https://doi.org/10.1038/s41586-024-07856-5

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Examples of literature reviews. Step 1 - Search for relevant literature. Step 2 - Evaluate and select sources. Step 3 - Identify themes, debates, and gaps. Step 4 - Outline your literature review's structure. Step 5 - Write your literature review.
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Divya Bhaskaran, Assistant Professor in the Exercise Science program of the Biology Department, published a research paper in the Frontiers in Neurology Journal. The article titled "Effects of an 11-week vibro-tactile stimulation treatment on voice symptoms in laryngeal dystonia" is a longitudinal clinical trial conducted during Dr Bhaskaran's post-doctoral work at the University of Minnesota.
RRB NTPC Previous Year Question Paper, Download PDF for CBT 1 and 2
RRB NTPC Previous Year Question Paper: Candidates appearing for the RRB NTPC exam can download the previous year papers from here for CBT 1 and CBT 2 exam. Check topic wise difficulty level and ...
Research Guides: Library Digital Collections SANDBOX: How do I cite an
Bibliography Entry Example. The UCLA Digital Library website. https://digital.library.ucla.edu (accessed June 28, 2024). Caption for an Image Inserted in Your Paper Example. Figure 1. The website for the UCLA Digital Library, Accessed August 22, 2024. Entire Website - Known Author (Chicago Manual of Style, 17th ed., sections 8.191, 14.206, 14.207)
A novel method to assess the integrity of frozen archival DNA samples
Methods in Ecology and Evolution is an open access journal publishing papers across a wide range of subdisciplines, disseminating new methods in ecology and evolution. Abstract Archival DNA samples collected and analysed for a range of research and applied questions have accumulated in the laboratories of universities, government agencies and ...
Navigating Scientific Articles
Primary research articles are typically organized into sections: introduction, materials and methods, results, and discussion (called IMRD). Identify key elements You may need to read an article several times in order to gain an understanding of it, but you can start by identifying key elements in a quick survey before you read.
Research on the Inheritance and Innovation Path of Minority Culture
As one of the traditional Chinese crafts, Yi embroidery carries rich cultural connotation and historical value. In recent years, with the implementation of rural revitalization strategy, the protection and inheritance of Yi embroidery culture has become an urgent problem to be solved. Through field research and literature research, this paper takes Nanhua County as an example to explore the ...
AI generates covertly racist decisions about people based on their
Hundreds of millions of people now interact with language models, with uses ranging from help with writing 1,2 to informing hiring decisions 3.However, these language models are known to ...

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Introduction:

A. Hemingway uses symbolism throughout the novel

B Thesis and preview of main points

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Writing Tips

Research for Fiction Writers: A Complete Guide

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Checklist: Dissertation Proposal

Examples: Edited Papers

Why is research important for fiction?

What should you be researching?

Character details and human behavior

Historical and social background

Writing style and genre

How should you be researching?

Incorporating research into fiction

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Your vs. You’re: When to Use Your and You’re

Novel - Free Essay Examples And Topic Ideas

Winston against the Party in the Novel 1984

How does Jack Represent Savagery in the Novel?

The Alchemist and the Pilgrimage by Paulo Coelho

Montag and Clarisse in the Novel Fahrenheit 451

The Problems in the Novel Fahrenheit 451

Deviations of the Hero’s Journey

Analysis of a Dystopian Novel Fahrenheit 451 by Ray Bradbury

The Novel “The Namesake” and Gogol Ganguli

Expressing Feminism in Pride and Prejudice by Jane Austen

Character Foil between Darcy and Wickham in the Novel Pride and Prejudice

Okonkwo is the Legend of the Novel Things Fall Apart

Socratic Seminar Slaughterhouse Five

Ready Player One: a Science-fiction Novel by Ernest Cline

Parallels between a Novel 1984 and Soviet Union

An Utopian Society in the Novel Animal Farm

George Orwell’s Fiction Novel 1984

Multiple Points of View Paper

A Political Novel 1984

Idyllic Society in the Novel Animal Farm

Main Theme in John Steinbeck’s Novel of Mice

The Color of Water a very Touching True Novel

Edgar Allan Poe Themes and Styles

Have you Ever Read a John Steinbeck Novel?

The Kite Runner a Novel Full of Betrayals

Dystopian Novel “Fahrenheit 451”

The Novel “Other Wes Moore”

Theme of Redemption in the Kite Runner

The Great Gatsby: Movie and Novel Adaptations

The Life of Gogul in the Novel the Namesake

Loneliness in Kokoro Novel

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Ancestral Trauma in Breath, Eyes, Memory by Edwidge Danticat

Research Template

Collect research for your next novel

How to use this template

1. Start with an empty template

Create a new board

Choose a template

2. Add any existing notes

Drag a note card onto your board

3. Save links to articles & news

Save content from the web

4. Save quotes & data

5. Collect video & audio

Embed Youtube videos or audio tracks in a board

6. Collect important images

Use the built-in image library

That's a great start!

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