Race and Socioeconomic Composition of the High Schools of National Football League Players
Bibliographic record
Abstract
This work built upon previous research examining meritocracy in elite sport by examining the socioeconomic and racial composition of the high schools of 1,881 players on National Football League (NFL) rosters in 2016. The NFL player data from pro-football-reference.com and perceived race data coded from player pictures are matched to school data for 23,785 public high schools in the Common Core of Data and 3,333 private high schools in the Private School Universe Survey. Using t tests of differences in group averages and General Linear Model analysis of variance, the authors found large statistically significant racial disparities within the NFL with Black NFL players attending high schools with an average of twice as many students in poverty and five times as many Black students than the high schools attended by White NFL players. Overall, NFL players attended high schools with lower socioeconomic status student bodies than the general student population, suggesting more meritocracy. However, analysis by player race shows the difference driven by the racial composition of the NFL compared with the general student population, suggesting this meritocracy is more complex; Black NFL players attended higher socioeconomic status schools with more White students than the general Black student population, and White NFL players attended higher socioeconomic status schools with fewer Black students than the general White student population.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".