Twenty-Five Years of Charles Mills’s <i>Racial Contract</i> in Sociology
Bibliographic record
Abstract
How have sociologists engaged the late philosopher Charles Mills’ landmark The Racial Contract (1997) in the twenty-five years since its publication? I first synthesize and periodize the corpus of sociological research citing The Racial Contract into two chronological and epistemological waves. The first wave (1997-2009) is distinguished by the scholarship of a vanguard who drew on the text, and direct engagement with Mills himself, in a paradigmatic shift away from the sociological study of race relations to the study of racism. The second wave (2010-present) is characterized by a fivefold increase in the text’s citation, tied to a resurgence of Du Boisian sociology and the early-career projects of a new generation of sociologists, as the text diffused from the sociology of race into other subfields of the discipline. I then go on to describe the influence of The Racial Contract on theory, data, and method in my own scholarship on racialization, first during my graduate studies in the United States, and later, as Sociology faculty in Canada at the University of Toronto, Mills’ alma mater. I end the essay with proposals for how a third wave of sociological engagement with The Racial Contract can more rigorously engage the text’s originating relationship to feminist political theory, as well as more actively be in dialogue with a new generation of critical philosophers who are already speaking back to us by centrally drawing on the work of sociologists.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".