Bibliographic record
Abstract
What role does affirmative action play in transitioning toward a more just society? The two literatures best equipped to answer this question — transitional justice and affirmative action — have neglected both the question and one another. Transitional justice scholars have focused on a limited set of measures (such as truth commissions and criminal prosecutions) and overlooked the role of affirmative action in facilitating transition. At the same time, affirmative action scholars have neglected the ways in which affirmative action may be part of a larger transitional justice project. Bringing these literatures into conversation for the first time, this Article shows how integrating affirmative action and transitional justice can advance our understanding of both practices. Affirmative action can bring attention to structural inequalities in transitional societies and help delineate the boundaries of transitional justice. In so doing, affirmative action can bridge a divide between the field of transitional justice and the phenomenon of societal transition that it seeks to understand and facilitate. Transitional justice, on the other hand, can elucidate how the period of transition informs affirmative action’s features and functions; it can also illuminate affirmative action’s strengths and shortcomings in bringing about a more just society. Affirmative action should, therefore, be added to the transitional justice “toolkit” and anchored in transitional justice concepts and debates.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.095 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".