Discrimination in a Rank Order Contest: Evidence from the NFL Draft
Bibliographic record
Abstract
Abstract This paper examines discrimination in the NFL draft. The NFL is a favorable empirical setting to examine the role of skin color because franchise selectors are required to make rank-order judgements of players based on noisy signals of future productivity. Since wages are tightly related to the rank-order of the draft for the first four years of a player’s career, even if discrimination plays only a marginal role in selection, there could be a large discriminatory impact. We observe racial differences in drafting. However, much of the variation is explained by Black and White players selecting into different playing positions. Conditional upon a large set of control variables, including athletic performance at a marque selection event (the NFL combine), we do not find robust evidence of racial discrimination in NFL drafting between 2000 and 2018. However, we do find some evidence that Black players are disadvantaged relative to White players in later rounds of the draft.
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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 | 0.000 |
| 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".