Exploring the Missing Link Between the Concussion “Crisis” and Labor Politics in Professional Sports
Bibliographic record
Abstract
This study focuses on connections between labor struggles in professional sports and the epidemic of concussions among athletes, specifically in the National Football League (NFL) and National Hockey League (NHL). Using a critical discourse analysis (CDA), we explain how popular media presents concussions in ways that are informative but often avoid a more politicized discussion of the athlete as a manual worker whose body succumbs to use and abuse of sport. We found two recurring themes in the North American popular press, including a tendency to (1) rely on a trope of “millionaires-versus-billionaires” to explain (and minimize) recent labor lockouts in the NFL and NHL and (2) shift focus on league deniability to athletes’ self-responsibility in the concussion “crisis.” Despite the urgency in which sports concussions and brain injuries have been reported in recent years, the two narratives work to discourage readers from recognizing how such health issues arise under specific relations of production, and that an athlete is a particular type of worker whose body is subjected to decline and disposability like so many other bodies under late capitalism. As we argue, working conditions are inseparable from concussions in professional sports, a phenomenon that requires further development within the popular press.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".