Equality and Merit: A Merit‐Based Argument for Equity Policies in Higher Education
Bibliographic record
Abstract
We assume, for the sake of argument, that the sole purpose of colleges and universities is the advancement of knowledge through teaching and research, and that academic merit, as defined by each discipline, ought to be the only relevant criterion in admissions and hiring decisions. Even on this restrictive set of assumptions, we argue that hiring and admitting women and people of color is sometimes the best way for colleges and universities to advance knowledge. We then address two objections to our argument, that race and sex are no more relevant than being left‐ or right‐handed, and that the epistemic attributes we ascribe to women and people of color belong to people as individuals, not as members of certain groups. We conclude that academic merit and social justice are mutually compatible.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.039 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".