Racial Diversity Deficit in College Football: Fixing the Pipeline
Bibliographic record
Abstract
A growing amount of research is being conducted on racial diversity in college football head coaching positions in the United States. However, very little has been conducted on the entry-level positions in college coaching: Graduate Assistants (GAs), Quality Control assistants (QCs) and restricted earnings coaches. These positions represent natural professional trajectories for student-athletes, who constitute the future pool of applicants for college coaching positions. In the United States, the majority of student athletes are nonwhite, but white coaches still dominate the world of college athletics. This paper investigates the pipeline issues that obstruct the matriculation of nonwhite student-athletes and produce what I call the diversity deficit in college football coaching. Existing analyses of empirical data from member institutions of the National Collegiate Athletic Association (NCAA) demonstrate the existence of racial inequality in the profession of coaching. This paper will explain the perpetuation of the diversity deficit by employing Critical Race Theory (CRT) to illustrate how whiteness, color-blindness and tokenism structure college football coaching. The paper then presents new research data that illuminate how power shapes NCAA member institutions and that can aid participants in addressing pipeline issues and the diversity deficit.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".