No More Mind Games: Content Analysis of In-Game Commentary of the National Football League’s Concussion Problem
Bibliographic record
Abstract
American (gridiron) football played at the professional level in the National Football League (NFL) is an inherently physical spectator sport, in which players frequently engage in significant contact to the head and upper body. Until recently, the long-term health consequences associated with on the field head trauma were not fully disclosed to players or the public, potentially misrepresenting the dangers involved in gameplay. Crucial to the dissemination of this information to the public are in-game televised commentators of NFL games, regarded as the primary conduits for mediating in-game narratives to the viewing audience. Using a social constructionist theoretical lens, this study aimed at identifying how Game Commentators represented in-game head trauma and concussions during NFL games for viewer consumption, through a content analysis of 102 randomly sampled regular season games, over the course of six seasons (2009-2014). Specifically, this research questioned the frequency and prevalence of significant contact, commentator representations of significant player contact, commentator representations of the players involved in significant contact and commentator communication of the severity of health hazards and consequences associated with significant contact. Observed during the content analysis were 226 individual incidents of significant contact. Findings indicate that commentator representations of significant contact did not appropriately convey the potential health consequences associated with head trauma and concussions to the viewing audience. Instead, incidents of significant contact were constructed by commentators as glorified instances of violence, physicality and masculinity- largely devoid and diffusive of the severity of health consequences associated with head injuries and concussions.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".