Bibliographic record
Abstract
In this paper, we illustrate a methodology to measure discrimination in educational contexts.In India, we ran an exam competition through which children compete for a large financial prize.We recruited teachers to grade the exams.We then randomly assigned child "characteristics" (age, gender, and caste) to the cover sheets of the exams to ensure that there is no systematic relationship between the characteristics observed by the teachers and the quality of the exams.We find that teachers give exams that are assigned to be lower caste scores that are about 0.03 to 0.09 standard deviations lower than exams that are assigned to be high caste.The effect is small relative to the real differences in scores between the high and lower caste children.Low-performing, low caste children and top-performing females tend to lose out the most due to discrimination.Interestingly, we find that the discrimination against low caste students is driven by low caste teachers, while teachers who belong to higher caste groups do not appear to discriminate at all.This result runs counter to the previous literature, which tends to find that individuals discriminate in favor of members of their own groups.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".