Theoretical foundation and empirical assessment of representation and meritocracy in academia
Bibliographic record
Abstract
Quantifying meritocracy directly is unfeasible because it requires large research efforts (such as surveys and controlled hiring experiments) that do not benefit the existing power structure. We circumvent this conundrum by proposing the use of openly accessible surname-publication data to quantify intergenerational representation in academia, which captures the socioeconomic aspect of diversity relative to the general population. We then use individual-based models of the intergenerational cycle of academic selection and reproduction to show that representation and merit in academia are entangled. We distinguish merit, or an academic candidate’s potential to produce given opportunities, from produced capital, including accomplishments before graduate school that only imperfectly predicts merit in a complex and changing world. Data from Harvard and US income groups and multiple independent model predictions all suggest that US academics are twice as likely as others to historically be academics by surname and underperform compared to a more representative academia, but individual-based affirmative action consistently raises academics’ mean merit. For academics aiming to tackle global crises, a lack of representation and merit may ultimately prevent actions necessary to avert disasters. This study reveals the magnitude of inequality, supports an individual justice foundation of affirmative action, and calls for recruitment evaluation that values merit over capital.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".