The Impact of Desegregation on College Choices of Elite Black Athletes
Bibliographic record
Abstract
Even a casual observer of American college athletics can see the emergence of star black athletes in conferences that once were racially segregated. By analyzing the college origins of National Football League and National Basketball Association draft choices between 1947 and 2011, this research measures the impact of higher education desegregation on the choices of elite African-American athletes in moving from historically black institutions (HBIs) to traditionally white institutions (TWIs). Using draft data and narrative descriptions, this paper documents when, why, and how this shift occurred. The desegregation of American education sometimes had the perverse effect of increasing opportunities for individual African-Americans, while subordinating the role or even extinguishing the black institutions serving that population in the Jim Crow era. In the desegregated era, there are some benefits to individual black athletes whose high professional draft status may make them young millionaires and to the states which have replaced a rigid odious racial color consciousness with fans cheering for university team colors worn by both their black and white athletes. But there is a price paid by the athletic programs of HBIs who are now confined to lower level conferences away from the most publicized contests. Some data reflected in the paper suggests that HBIs are losing in the competition to recruit elite black students in non-athletic fields as well.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".