Bibliographic record
Abstract
This paper contributes to recent debates over the place of race in liberal theory, and the work of John Rawls in particular. Controversy has centered on whether Rawls’s broader philosophical approach is capable of addressing racial injustice, and if not, precisely why the Rawlsian framework remains disconcertingly blind to such issues. Pace scholars who focus on Rawls’s emphasis on “ideal theory,” and whether that precludes his engagement with racial domination, we show that Rawls’s inability to account for, or address, racial injustice lies in his limited understanding of the kinds of “associations” or institutions that condition and perpetuate racial oppression. As studies in race and American Political Development have shown, nonstatutory institutions such as political parties, unions, and universities were key to the development and maintenance of racial hierarchical order. Fully understanding the role of these institutions in perpetuating racial injustice allows us to see that the limitations of Rawls are not his ideal theory, per se, but his preoccupation with the “basic structure” of society, which rendered such institutions outside his analysis. We conclude by drawing on thinkers in the Afro-Modern tradition who help us conceptualize how such institutions are complicit in, and can be weaponized against, racial domination.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".