Racism and black male student-athlete experiences in a Canadian University
Bibliographic record
Abstract
This study investigates anti-Black racism and the experiences of Black male student-athletes. Specifically, using counter-stories we examine Teshawn’s experiences as a Black male former student-athlete on the varsity football team. In doing so, we also draw on the experiences of other Black student-athletes through a publicly commissioned report detailing incidents of anti-Black racism at the same university. The first composite counter-story ‘Get Out of the Drill!’ highlights racism by the white coaches and the implications for Black student-athletes in their current and future athletic opportunities. The second story, ‘Don’t Listen to Them. Block the Noise!’ highlights racism by the white coaches and white teammates and the negative implications it has on the Black male student-athletes’ academic success. Finally, the third story ‘Racist Jokes and Slurs are Never-ending’ emphasizes the declining mental health and fatigue of Black male student-athletes as reports of racism are ignored and mishandled by the institution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.051 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".