Concussion Reporting and Racial Stereotypes: ESPN’s Role in Shaping Public Perception About Athletes of Color
Bibliographic record
Abstract
In the 2022 National Football League (NFL) season, Miami Dolphins’ quarterback, Tua Tagovailoa, received two concussions in 5 days and was taken off the field on a stretcher. The media framing around Tagovailoa’s concussions was focused on the flaws in the NFL concussion policy and the poor decision making of the neurotrauma consultant. However, no mention of Tagovailoa’s race was mentioned despite historical racist practices regarding concussions in football for racialized athletes. Given the media’s role in the framing of concussions and the perpetuation of racial stereotypes, I conducted a content analysis to explore ESPN media articles dedicated to concussion stories during the 2022 NFL season. Ultimately, this paper concludes that through subtle but pervasive frames, the writers at ESPN continue to perpetuate racial stereotypes that construct racialized athletes as physiologically superior, intellectually inept, and criminally dangerous.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".