Bibliographic record
Abstract
We argue that in important circumstances meritocracy can be realized only through a specific form of affirmative action we call affirmative meritocracy. These circumstances arise because common measures of academic performance systematically underestimate the intellectual ability and potential of members of negatively stereotyped groups (e.g., non‐Asian ethnic minorities, women in quantitative fields). This bias results not from the content of performance measures but from common contexts in which performance measures are assessed—from psychological threats like stereotype threat that are pervasive in academic settings, and which undermine the performance of people from negatively stereotyped groups. To overcome this bias, school and work settings should be changed to reduce stereotype threat. In such environments, admitting or hiring more members of devalued groups would promote meritocracy, diversity, and organizational performance. Evidence for this bias, its causes, magnitude, remedies, and implications for social policy and for law are discussed.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".