“You have 60 minutes to do what you can’t do in real life. You can be violent”: young athletes’ perceptions of violence in sport
Bibliographic record
Abstract
Various forms of violence against youth are documented in sport. To date, young athletes’ perceptions of violence in sport remain relatively unstudied. The objective of this study was to examine how violence and its various manifestations in sport have been understood by young athletes. In total, 60 athletes from a variety of sports and ages (12–17 years old) participated in nine semi-structured focus groups. The interview data were submitted to a thematic analysis using NVivo. Results obtained showed that various motivations for participating in sport influenced the ways in which young athletes addressed violence in this context. Additionally, the findings showed that violence in sport is a concept that young athletes partially understand. Even if most of them described various forms of violence in sport, some forms were misunderstood or have not been addressed at all. Finally, young athletes provided their own explanations of this issue in sport. From their perspective, violence in sport can be seen as part of the sport, a strategy to achieve competitive success on the field, a protective mechanism or a result of the valorisation of violence in sport by peers, parents, coaches and sport organisations. Considering that some young athletes normalised violence in sport, it seems crucial to make prevention efforts targeting social norms in sport.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".