Bibliographic record
Abstract
Abstract At first glance, the topic of sports aggression and violence directs our attention to what happens on the field of play during athletic contests between competitors both within and outside accepted rule structures. Indeed, this is the way the subject matter has traditionally been defined and approached. Such an approach is both inescapable and useful. But the fact of the matter is that it is necessary to step back and consider the sociological underpinnings, outcomes, and associations of athlete aggression and violence. As such, a cluster of related issues quickly becomes apparent: why and how various sports require athletes to play in an aggressive manner; why aggressive socialization strategies are not embraced by all athletes in the same way; how coaches and administrators might play contributory roles; how risky play is linked with pain and, in turn, how injury is linked with litigation. Such questions inevitably bring larger sociological factors into focus, such as social control, social stratification, and social change. Rather than viewing athlete aggression and violence in isolation, this chapter considers these issues through the lens of existing debates to place the subject matter in broader and more expansive sociological context.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".