Exploring the Influence of Social Categorization on the Perception of Antisocial Behavior in Sport
Bibliographic record
Abstract
Despite the considerable body of research dedicated to understanding antisocial behavior in sports, little is known about the perceptions of principal stakeholders (e.g. coaches, referees, and sports organization board members) of such behaviors. In this study, we qualitatively explored the meaning of antisocial behavior in sports using a social identity approach from the perspective of multiple key sports stakeholders. Twenty-one participants belonging to various social categories (i.e. coaches, athletes, athletes’ relatives, referees, and sports organization board members) participated in semi-structured interviews aiming to explore what is and what is not antisocial behavior in sports. Contrary to the current understanding of what is antisocial in sports, findings revealed that antisocial behavior was primarily associated with (a) being violent, (b) failing to conform to social norms and values, (c) having non-justifiable discrimination practices, (d) expressing dysfunctional emotional states, (e) manipulating, (f) overstepping one’s role, (g) cheating and (h) communicating in an inappropriate way regarding the target. Results of this explorative study also indicated that events being viewed as antisocial vary depending on the participant’s categorization level. Collectively, these findings highlight the importance of considering social categorization (and its implications) to better understand the concept of antisocial behavior in sports.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".