Bibliographic record
Abstract
Abstract In this paper I respond to Robert Taylor's argument that a Rawlsian framework does not support strong affirmative action (AA) programs. The paper makes three main arguments. The first disputes Taylor's claim that strong AA would not be needed in ideal conditions. Private racial discrimination, I suggest, might still exist in such conditions, so strong AA might be needed there. The second challenges Taylor's claims that pure procedural justice constrains Rawlsian nonideal theory. I argue that this rests on a fetishizing of pure procedural justice that is absent from Rawls's work. I also show that a revised formulation of Taylor's concern here also fails. My third argument makes a positive Rawlsian case for strong AA in nonideal conditions that builds on a Taylor concession. Taylor suggests that the goal of nonideal theory is to create a world in which ideal theory can be applied. My argument begins by showing that another permissible goal of Rawlsian nonideal theory is to ameliorate injustice. I then argue that Rawls's contractualist framework supports the strongest forms of AA (categories 4–5 interventions) when category 3 interventions are blocked.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".