Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it