Scaling Down Inequality: Rating Scales, Gender Bias, and the Architecture of Evaluation
Bibliographic record
Abstract
Quantitative performance ratings are ubiquitous in modern organizations—from businesses to universities—yet there is substantial evidence of bias against women in such ratings. This study examines how gender inequalities in evaluations depend on the design of the tools used to judge merit. Exploiting a quasi-natural experiment at a large North American university, we found that the number of scale points used in faculty teaching evaluations—whether instructors were rated on a scale of 6 versus a scale of 10—significantly affected the size of the gender gap in evaluations. A survey experiment, which presented all participants with an identical lecture transcript but randomly varied instructor gender and the number of scale points, replicated this finding and suggested that the number of scale points affects the extent to which gender stereotypes of brilliance are expressed in quantitative ratings. These results highlight how seemingly minor technical aspects of performance ratings can have a major effect on the evaluation of men and women. Our findings thus contribute to a growing body of work on organizational practices that reduce workplace inequalities and the sociological literature on how rating systems—rather than being neutral instruments—shape the distribution of rewards in organizations.
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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 | 0.003 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".