Bibliographic record
Abstract
Comics are an important art form that involve images/drawings, often combined with text. They can deliver both meaningful and enjoyable or provocative content on important topics. is one such topic – this brain injury can lead to debilitating physiological, psychological, and social consequences, and has become a hot topic in the public eye. In particular, concussions have been highlighted with respect to professional sport. However, this injury occurs in all levels of sport and is also a public health problem in other contexts of life (e.g., in transportation and in the workplace). The aim of this study is to explore the themes related to concussions that are reflected in this form of popular media. Forty-three publicly available comics were identified using the search terms Concussion + comic and Sport concussion + comic in the Google search engine. A content analysis was used to analyze these comics. The results provide an important look at the types of stereotypes, misinformation, exaggerations, correct information, and emphases that are perpetuated through this creative medium. This information allows us to recognize the messaging that is available for public consumption, and could add to our understanding of this source of knowledge. The most common themes that arose from this analysis include fear (of injury or reinjury), use of fear tactics, minimizing the gravity of the injury, the commodification of athletes, and the culture of sport. A critical discussion provides considerations for the intentional production and consumption of comics on concussions and beyond.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".