import {viewof dataSummaryView, Tabulator, viewof selectedColumns, viewof dataSet, tableContainer, fetchData, generateTabulatorTableFromCSV, progress, progressbar} from "8bb63a6cde9addff"Selected British Literary Prizes (1990-2022)
prizes
british
dataset
race
gender
Abstract
The Selected British Literary Prizes (SBLP) dataset contains information on nine major literary prizes in the U.K. from 1990 to 2022 and demographic information on 682 prize winners and shortlisted authors.
Data Table
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Creative Commons License
This work is licensed under CC BY 4.0
Significance and Context
The prizes in this dataset were included based on their prestige, longevity, and an eye towards generic representation of various kinds of fiction, poetry, drama, and non-fiction. They are the James Tait Black Memorial Prize (Fiction and Drama), Costa Book Award (Book of the Year, Novel, First Novel, Poetry, Biography, Childrens), The Booker Prize, Women’s Prize for Fiction, The Gold Dagger Award, The British Science Fiction Association Award, The T.S. Eliot Prize for Poetry, The Ted Hughes Award for New Work in Poetry, and The Baillie Gifford Prize for Non-Fiction. This dataset contains primary categories of information on individual authors comprising gender, sexuality, UK residency, ethnicity, geography and details of educational background, including institutions where the authors acquired their degrees and their fields of study. Along with other similar projects, we aim to provide information to assess the cultural, social and political factors determining literary prestige.1 Our goal is to contribute to greater transparency in discussions around diversity and equity in literary prize cultures.
Background and Scholarship
British literary prizes have been mired in controversies involving selection of juries, their financial entanglements, representation of women and non-binary authors and those from racial and ethnically diverse backgrounds among shortlisted authors.2 Pierre Bourdieu’s concepts of the field of cultural production and symbolic capital are the basis of several important studies of prizes. Very often discussions of British literary prizes have focused on the Booker awards (Auguscik, Cachin and Ducas-Spaed, Norris, Todd, Strongman). In other cases, critics have examined the relationship between prizes and national literary traditions when those traditions are somewhat ex-centric to metropoles as is the case with Canadian and Scottish literature. Examples of this work are Gillian Roberts’s study of prize-winning Canadian authors and Stevie Marsden’s recent analysis of Scottish national literature through the history of the Saltire prize.
Pascale Casanova in The World Republic of Letters (2004) and James English in The Economy of Prestige (2005) invoke and contest Bourdieu’s sociological insights to discuss implicit assumptions and biases in literary prize cultures. Prizes, English says, “serve simultaneously as a means of recognizing an ostensibly higher, uniquely aesthetic form of value and as an arena in which such value often appears subject to the most businesslike system of production and exchange.” Recognizing that cultural value is always influenced by hierarchies of class, race, gender, or nation, English offers a sociological analysis of contemporary prize culture that is attentive to its market imperatives but does not consider these as the sole arbiter of value or capital. Our research also resonates with Casanova’s thesis about the world literary space or republic of letters constituted and consolidated in European cities such as Paris and London where prizes are one mode of currency in the literary marketplace. Thus, the emergence since the 1980s of writers of African, Asian, Caribbean, and Arab backgrounds in prize lists can be taken as a sign of the ‘worlding’ of British prizes.
These incisive analyses hinged on nationality sometimes pay insufficient attention to gender, race and/or ethnicity when outlining the contours of the world literary space. Even as prizes gild the reputation of contemporary authors and works with symbolic capital, their reputations are sometimes tarnished by explicit and implicit biases. Other factors are location of prize centers, jury and committee compositions and the influence of corporate publishers. Nevertheless, authors and publishers capitalize prize-winning books in various ways. In the SBLP data the language (English) and the location of prizes (Britain), necessitates a realistic look at literary recognition by the metropole of works by authors from other parts of the world, including Britain’s former colonies. In the late twentieth century and twenty-first century these prizes can also be seen as participating, being influenced by, and in turn influencing the prize among prizes, the Nobel. Six male authors and a woman author in the data – J.M. Coetzee, Abdulrazak Gurnah, Seamus Heaney, Kazuo Ishiguro, Toni Morrison, Orhan Pamuk, and Derek Walcott – were shortlisted or won one or more of these prizes and the Nobel Prize for Literature.
Using this Dataset
This data indicates trends in the generation of literary and cultural capital. It will thus be useful to students, scholars, teachers, and literary and cultural historians of contemporary world literature who would like to know more about the relationship between the cultural formation of writers (their gender, sexuality, location, ethnicity, education) and the garnering of literary capital through the awarding of prizes. It will also be of interest to other researchers who are developing similar projects, especially on U.K. or non-U.K. prizes not covered here. As well, researchers might find the ethnicity macro categories we developed useful when considering demographic information on literary authors.
The data can contribute to answering questions such as:
- How many British writers versus writers with affiliations beyond Britain have been shortlisted/awarded these prizes?
- Have prizes improved their record on gender and/or ethnic representation in shortlists and awardees?
- What are the economic consequences of prizes for the writer’s career in publishing and media?
- Is there a connection between specific educational credentials and/or educational institutions and writers’ chances of being shortlisted or winning?
- Are there identifiable trends in the literary styles of shortlisted or winning works in specific years or decades?
- In which genres are women, Black, Asian and ethnically diverse writers most likely to be shortlisted and/or awarded?
We hope that besides researchers, prize committees who wish to examine the history of racial, ethnic and gender (dis)parities in a particular award category will now have the SBLP data to make a case for diverse ethnic, gender, and geographical representation.
Collection and Creation
The SBLP dataset was created by Katherine Binhammer, Kanika Batra, Theo Gray, and Maryse Jayasuriya as part of ongoing research for The Orlando Project: Feminist Literary History and Digital Humanities. It draws on research for Volume Four: Revisioning the Contemporary of a four-volume narrative history based on the Orlando textbase (Cambridge UP). Our research goal was to assess the role major British literary prizes play in promoting and circulating contemporary literature and to determine whether the shortlist and winners are representative of a multicultural Britain. PhD candidate Theo Gray did the heavy lifting of data compilation, and their work was compensated with a research assistant stipend from the University of Alberta and the Social Science and Humanities Council of Canada. The three other scholars are senior faculty at research-intensive universities whose work is incentivized by their institutions in various ways including course releases, sabbaticals, research funding, and merit salary increases.
Data collection began with collaborators deciding upon a list of prizes based on longevity, award monies, genre coverage, and reach within and beyond Britain. Even within these parameters collaborators decided one way to prioritize information on specific awards was by 1) the year in which they were founded; and 2) prize money. Theo Gray hand compiled the data on each author in consultation with Katherine Binhammer based on an intersectional feminist approach which included the following: gender, sexuality, UK residency, ethnicity classifications, ethnicity, highest degree, degree institution country, degree institution, degree field category, degree field. Kanika Batra and Theo Gray discussed and arrived at the macro and parsed ethnicity categories. The main sources of information on each author were prize websites, author websites, author bios, profiles, obituary articles, interviews, podcasts, and recorded readings. Wherever possible, scholarly sources such as encyclopedias and biographical dictionaries were consulted, though because our focus was on the past few decades, such material was limited. Maryse Jayasuriya and Kanika Batra cross-checked the parsed ethnicity information with categorical complexities noted for further questioning. Collaborators met from January 2024-May 2025 to clarify and refine various categories balancing granularity and generality.
Metadata Discussion
Prize names: For purposes of aggregation, we decided to normalize historic prize names to their contemporary forms in addition to their prize_id column. This was done with an additional column (prize_alias) that clarifies that, for instance, the “Whitbread Biography,” “Whitbread Biography Award,” and “Costa Biography” are all part of the same prize series even though they have gone by different names at different times. The same is the case with the “Booker” and the “Man Booker.”
Prize dates include prizes awarded from 1990 to 2022. Although some of the prizes in the SBLP data were instituted much earlier, these dates were decided to indicate recent trends among shortlisted and winning authors. Further, the establishment of newer awards like Women’s and Ted Hughes allows for comparisons across older ones such as James Tait and the Booker within a similar time frame without creating too many data discrepancies.
Ethnicity was labeled primarily by the ways in which authors identified themselves. The sources were the same as those for compiling the data on the authors, with the 2021 UK census classifications and UK Guidelines for Writing about Ethnicity as points of reference. Our aim is to “decouple ethnicity, as it functions in the dominant discourse, from its equivalence with nationalism, imperialism, racism and the state, which are the points of attachment around which a distinctive British or, more accurately, English ethnicity have been constructed” (Hall). A further discussion of the knotty ethical considerations of collecting ethnicity data is included below.
Gender and Sexuality of an author is understood to be fluid. The author’s self-identification of gender was prioritized over other gender assignments. Some gender assignments and pronouns may have subsequently changed from the three used in the data: “man,” “woman,” and “non-binary.” Unless self-identified as “queer” (which includes LBGTQ2+), an author’s sexuality is listed as “unknown,” to indicate that sexuality exists on a continuum between straight and queer, and to indicate that many straight authors do not feel the need to identify their sexuality explicitly, though it may be deduced from their biographies or interviews. We include sexuality because considering it allows for a politics of ethnicity premised on diversity and difference to emerge (Hall) and because it undermines the idea of cis hetero white Englishness as an unmarked but nevertheless normative ethnicity. Roughly six percent of the authors in the dataset identify as queer, and one author identified himself as straight.
Educational classifications were based on the author’s highest degree. Data collected includes the institution where the authors acquired their degrees, degree institution country, and the level of highest degree attained. In addition, a set of degree field macro-categories was created to track patterns in academic training, while preserving data on the degree fields (which varied from person to person and institution to institution). The categories were Writing, Arts, History and Cultural Studies, Language and Literature, Law, Math, and Sciences, Philosophy and Theology, Politics and Economics, Medicine and Social Work, Multiple, none, and unknown. Here too, the information provided by the author was the first basis of the labels. In some cases, one or more of these pieces of information was not available. Where either the degree field, country, institution, or degree, or more than one of these was not traceable with certainty, it was listed as “unknown.”
Linked Data: Finally, where viaf codes could be found for authors, these are included.
Description
The columns in the dataset include:
prize_id: unique prize identifier used in the SBLP dataset
prize_alias: name of the prize awarded, regularized to the most current name
prize_name: name of the prize awarded, at the time of award
prize_institution: institution that sponsored the prize
prize_year: year the prize was awarded
prize_genre: genre category of book that the prize was awarded to
person_id: unique author identifier used in the SBLP dataset, assigned in order of entity entry to the dataset
person_role: whether author was shortlisted or won the prize
last_name: family name of author
first_name: given name of author
name: full name author in family name, given name format
gender: author’s gender, as self-declared/publicly available
sexuality: author’s sexuality, as self-declared/publicly available
uk_residence: whether the author holds residence status in the UK at the time of data gathering
ethnicity_macro: ethnicity macro category, as created for this dataset
ethnicity: ethnicity as self-declared/publicly available
highest_degree: highest level of post-secondary education
degree_institution: institution from which the highest degree was attained
degree_field_category: degree macro category, as created for this dataset
degree_field: field of study, as self-declared/publicly available
viaf: virtual internet authority file code
book_id: unique book identifier used in the SBLP dataset
book_title: title of the awarded or shortlisted book
Ethical Considerations
All of the information in this dataset is publicly available. Information about a writer’s location, gender identity, race, ethnicity, or education from scholarly and public sources can be sensitive. The data provided here enables the study of broad patterns and is not intended as definitive.
Requests to correct, amend, or remove any sensitive information will be honored. Please contact Katherine Binhammer (kb1@ualberta.ca) or Kanika Batra (kanika.batra@ttu.edu) with any questions or concerns.
Notes on Ethnic Identifications
Collecting data to group authors’ ethnicity and making general claims about writers is perhaps the most challenging component of our research. Following important Black cultural studies work we recognize that both race and ethnicity are “discursive constructs” and “sliding signifiers.” Further, ethnic groups in official UK data combine different aspects of individual identity, and many of the categories are informed by historical ideas of race (Mirza). UK government guidelines for writing about ethnicity explicitly state that they refer to ethnicity and not race because: surveys usually ask people for their ethnicity and not their race; using consistent terms helps people to understand their data. We are aware that these guidelines obscure racial identifiers and racial biases.
We began by using the UK census categories but quickly found that they were not adequate to the complexities of our specific needs since they apply to UK citizens whereas our dataset includes international authors. Thus, we assembled three tiers of data. Self-identification of authors was recorded, as well as the UK census category if the author resided in the UK. In addition, all authors, both UK residents and international, were grouped into ethnicity macro categories. As the SBLP collects data on UK-based prizes, the ethnicity categories under which authors are grouped have a UK slant, even though sometimes these may not be as valid or as applicable in the case of international authors where other factors might have greater relevance.
Once the data had been compiled, a major question which emerged was granularity and whether it was possible to group certain definitions to make larger claims about white and non-white identities. As this is a common concern in data representation, we talked about the possibility of collapsing ethnicity classifications. Here we worked simultaneously with geographical location, cultural background, racial self-identification, and UK ethnicity categories. We worked with race, ethnicity and national assignments when we collapsed White Canadian, White American, White Australian, White New Zealander, White South African, Bulgarian, Croatian-Iranian American, Iranian American, Turkish American, Icelandic, Spanish, Dutch, German, Swedish, Albanian, Slavic Australian, English-Canadian into “Non-UK White.” Similarly, in “Estonian British,” “Chinese British,” or “British Mauritian,” the placement of British or another nationality first depended on various factors. Some authors hold dual nationalities while others claim British as their primary national identity though they may have lived in another country for most of their writerly careers. A select few may place either nationality first depending on where their books are published and the primary readership they hope to attract. Finally, some diasporic authors claim British nationality as primary though they may not yet have acquired formal UK citizenship.
We faced a similar ethical dilemma when deliberating whether to include Trinidadian British, Jamaican Chinese British, British Guyanese, Jamaican British under “British Caribbean,” as it flattens distinct Caribbean national identities. Moreover, as “British Caribbean” is more accepted than “Caribbean British,” the former does not accurately represent authors who place their island Caribbean identities foremost. We finally decided on “Caribbean,” as a macro category. Another added complication is that some ethnic groups are also nationalities such as Bangladeshi, Chinese, Indian, and Pakistani. Often there are clear cultural and religious connections between some of these groups; at other times these connections are less cultural than regional or geographic. We made considered decisions regarding other macro categories. Indian, Pakistani, Bengali, Bangladeshi, Malaysian, Sri Lankan, Hong Kong authors can be categorized as “South Asian” and/or “Asian.” We ultimately decided on “Asian” as a more capacious, though less geographically specific and culturally connected category.
A few examples may help explain these complications. A major literary figure such as Caryl Phillips is subsumed under the macro ethnicity category “Caribbean” though in fact he was born in St. Kitts, educated and wrote in Britain, and has been based in the US for several decades. Phillips’ ethnicity in the appended Ethnicity Data Table as “Kittian British American” allows for capturing some of his multiple affiliations. Similarly, Gillian Slovo was born inSouth Africa but has lived in Britain since her family were political exiles there during the apartheid years. Her ethnicity comprises White South African roots and British domicile. Other instances of controversial categorization may be American born authors, Ottessa Moshfegh and Azadeh Moaveni, both of whom have Iranian parents, listed as “Non-UK White” in the ethnicity macro category.
We hope the ethnicity table accompanying the dataset will be useful to researchers who wish to examine the proximities of ethnicity to race, religion, nationality, language and sometimes country of domicile. As the administrative centers of the prizes are in the UK, the ethnic macro categories remain relevant to the data, despite these complications and contestations. Ethnicity data, even when categorized according to tidy data principles, remains complicated. Researchers who wish to categorize authors differently than the macro categories we use may do so using the parsed identities in the ethnicity table.
With these qualifications. ethnicity as used in the data accomplishes the following goals:
- challenges pseudo-conceptualization and harmful actualization of race as a vertical hierarchy of white and Black/non-white by positing ethnicity as a horizontal relationality of cultural similarities/differences within and between groups;
- bypasses the biological essentialism and normative binarism that is currently associated with rigid racial classifications;
- expresses the most effective articulations of ethnicity as cultural identities impacted by processes of globalization including the flow and movement of people, ideas, goods, capital, and technologies, though assimilationist hegemonic versions of ethnicity exist.
Ultimately, we developed the following macro categories which combine race, ethnicity, national affiliation and/or geography (the table below outlines the sub-categories): African, Asian, White American, Non-White American, Black British, Caribbean, Irish, Jewish, Non-U.K. White, unknown and White British. In Britain, as in other parts of the world mentioned in the macro categories, it is often difficult to distinguish various mixed-race ethnicities. We placed mixed race authors in the ethnic macro categories we developed, as explained at the beginning of this paragraph. Authors like Zadie Smith (one white and one black parent) or Hannah Lowe (one white and one mixed-race parent) are listed as Black British or Caribbean respectively based on the way they situate themselves in interviews, writings, promotion and publicity. Mixed race authors are not counted twice in the data.
Data Table
Download Table Data (including filtered options)
Asian: Bangladeshi; Bengali; British Bangladeshi; Cantonese British; Chinese; Chinese American; Chinese British; Chinese Canadian; German Indian; Hong Kong; Indian; Indian American; British Indian; Indian Canadian; Indian English; Indian Welsh; Japanese; Japanese American; Malaysian; Pakistani; Pakistani Scottish; Sri Lankan; Sri Lankan Tamil; Sri Lankan Sinhalese; Vietnamese American; Vietnamese French American
Non- White American: Black American; Ethiopian American; Ghanaian American; Dominican American; Indian American; Indigenous American; Mexican
Black British: Black British; Black Scottish; British Guyanese; British Kenyan; British Mauritian; British Nigerian; Jamaican British; Nigerian Scottish; Scottish Sierra Leonian; Somali British; Trinidadian British; Zambian British
Caribbean: Jamaican; Jamaican Chinese British; Kittitian; Mixed Caribbean; St Lucian; Trinidadian
Irish: British Irish; Irish; Irish American; Irish Canadian; Scottish Irish
Jewish: Dutch Jewish; Jewish; Jewish American; Jewish Australian; Jewish British; Jewish Canadian; Jewish English; Jewish South African; Welsh Jewish
Non-UK White: Albanian; Belarusian; Belgian British; Bulgarian; Canadian; Canadian American; Dutch; Dutch Sri Lankan; English-Canadian; Estonian British; French Canadian; French, English, Dutch; German; German American; German Australian; Greek American; Hungarian; Hungarian British; Hungarian English; Icelandic; Italian; Italian American; Russian Canadian; Scottish American; Serbian American; Slavic Australian; Spanish; Swedish; Turkish; Turkish American; Ukrainian; White American; White American Canadian; White Australian; White Canadian; White New Zealander; White South African
White British: American British; British; British Australian; Canadian British; English; English American; English French; French British; Italian British; Northern Irish; Norwegian British; Scottish; Turkish English; Welsh; Welsh Maltese; Yugoslavian British
African: Tanzanian, Egyptian, Ghanaian, Nigerian
Ethnic categorization is fraught and can only ever be imperfect as there are no stable categories for understanding ethnicity, especially in relation to its obvious metonymic proximities to race, gender, nationality, religion and geography and not-so-obvious proximities to gender and sexuality. Nevertheless, categorization is a necessary part of the process of researching racial, ethnic, and gender inequities in prize culture.
Endnotes
1. Other such projects include: The Index of Major Literary Prizes in the U.S. by Claire Grossman, Juliana Spahr, and Stephanie Young, (Post-45 Data Collective) and the work of Nicola Griffith and other members of the Literary Prize Data group (nicolagriffith.com/2015/09/08/update-on-literary-prize-data-group/). The first, The Index of Major Literary Prizes in the U.S. is far more comprehensive than ours; Grossman, Spahr, and Young include 1800 authors, information on 1100 judges and cover 41 prizes in total, from 1918-2020.
The second, Nicola Griffith’s Literary Prize Data Group, sought to “assemble data on English-language literary prizes to get a picture of how gender bias operates within that ecosystem” and covered the Saltire, Asian Man Booker, Hugo, Nebula, Carnegie, Campbell Memorial, Bailey’s/Women’s, and Australian and New Zealand awards. Some of these are the same prizes we focus on. Though Griffiths does not publish an open dataset and the site’s last updates were in 2015, she does provide good information about gender disparities in winners as well as the male focus of the books recognized and rewarded. Neither the race nor the ethnicity of the prizewinners is considered in the findings.
2. UK Guidelines for Writing about Ethnicity state:
“We do not use the terms BAME (black, Asian and minority ethnic) and BME (black and minority ethnic) because they emphasize certain ethnic minority groups (Asian and black) and exclude others (mixed, other and white ethnic minority groups). The terms can also mask disparities between different ethnic groups and create misleading interpretations of data.
In March 2021, the Commission on Race and Ethnic Disparities recommended that the government stop using the term BAME.
One of the recommendations in the final report on COVID-19 disparities, published in December 2021, was to refer to ethnic minority groups individually, rather than as a single group.
This was supported by research commissioned by the Race Disparity Unit (RDU), which found that people from ethnic minorities were 3 times more likely to agree than disagree that the term ‘BAME’ was unhelpful.”
Publications by the Runnymede Trust, a British race equality and civil rights think tank, use Black and Minority Ethnic (BME) in their reports on racial and ethnic disparities in the UK. Although BME is widely used instead of the now out of favor BAME, we follow The University of Manchester’s Center on the Dynamics of Ethnicity Report, The impact of Covid-19 and BLM on Black, Asian and ethnically diverse creatives and cultural workers, in using ‘Black, Asian and ethnically diverse’ as the collective term to refer to racial and ethnic minorities, shortened to ‘ethnically diverse,’ sometimes. Where possible we refer to specific groups.
Bibliography
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Citation
BibTeX citation:
@article{binhammer2025,
author = {Binhammer, Katherine and Batra, Kanika and Gray, Theo and
Jayasuriya, Maryse},
editor = {Manshel, Alexander and Porter, J.D. and Walsh, Melanie},
title = {Selected {British} {Literary} {Prizes} (1990-2022)},
journal = {Post45 Data Collective},
date = {2025-06-11},
url = {https://data.post45.org/posts/british-literary-prizes/},
doi = {10.18737/CNJV1733p4520221212},
langid = {en},
abstract = {The Selected British Literary Prizes (SBLP) dataset
contains information on nine major literary prizes in the U.K. from
1990 to 2022 and demographic information on 682 prize winners and
shortlisted authors.}
}
For attribution, please cite this work as:
Binhammer, Katherine, Kanika Batra, Theo Gray, and Maryse Jayasuriya.
2025. “Selected British Literary Prizes (1990-2022).”
Edited by Alexander Manshel, J.D. Porter, and Melanie Walsh. Post45
Data Collective, June. https://doi.org/10.18737/CNJV1733p4520221212.