Bibliographic record
Abstract
This article discusses the adequacy of Rawls’ theory of justice as a tool for racial justice. It is argued that critics like Charles W Mills fail to appreciate both the insights and limits of the Rawlsian framework. The article has two main parts spread out over several different sections. The first is concerned with whether the Rawlsian framework suffices to prevent racial injustice. It is argued that there are reasons to doubt whether it does. The second part is concerned with whether a Rawlsian framework has the resources to rectify past racial injustice. It is argued that it has more resources to do this than Mills allows. This second part of the article centers on two Rawlsian ideas: ideal theory and the fair equality of opportunity (FEO) principle. It is argued that ideal theory is essential for the kind of rectificatory work that Mills wants nonideal theory to do, and that where there is a socioeconomic legacy of past injustice, it is hard to see how FEO could be implemented if it did no rectificatory work, a result which means that there is less need to turn to nonideal theory at all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".