Tokenism and Its Long-Term Consequences: Evidence from the Literary Field
Bibliographic record
Abstract
Research on tokenism has mostly focused on negative experiences and career outcomes for individuals who are tokenized. Yet tokenism as a structural system that excludes larger populations, and the meso-level cultural foundations under which tokenism occurs, are comparatively understudied. We focus on these additional dimensions of tokenism using original data on the creation and long-term retention of postcolonial literature. In an institutional environment in which the British publishing industry was consolidating the production of non-U.S. global literatures written in English, and readers were beginning to convey status through openness in cultural tastes, the conditions for tokenism emerged. Using data on the emergence of postcolonial literature as a category organized through the Booker Prize for Fiction, we test and find for non-white authors (1) evidence of tokenism, (2) unequal treatment of those under consideration for tokenization, and (3) long-term retention consequences for those who were not chosen. We close with a call for more holistic work across multiple dimensions of tokenism, analyses that address inequality across and within groups, and a reconsideration of tokenism within a broader suite of practices that have grown ascendent across arenas of social life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".