Examining the role of descriptive norms in the social identity and moral behaviour relationship in youth sport
Bibliographic record
Abstract
In youth sport settings, social identity (i.e., identification with a team) and descriptive norms (i.e., standards of acceptable behaviours) have been linked to moral behaviour (e.g., Bruner et al., 2014; Shields et al., 2005). In recent work, norms have demonstrated a stronger association with behaviour when the group is personally meaningful (Spink et al., 2013). Thus, we investigated whether descriptive group norms influenced the relationship between social identity and moral behaviour. Male and female athletes (N = 378) from 28 competitive youth ice hockey teams completed measures of social identity (ingroup ties [IGT], cognitive centrality [CC], ingroup affect [IGA]; Bruner et al., 2014) and self-reported prosocial and antisocial behaviour toward teammates and opponents (PABBS; Kavussanu & Boardley, 2009). Team norms represented perceptions that teammates performed the moral behaviours assessed in the PABBS. Multilevel analyses revealed a significant interaction between IGA and team norms for prosocial behaviour toward teammates (PBT; p = 0.01). Simple slopes analysis revealed when team norms for PBT were high, those with high IGA (i.e., positive feelings toward the team) reported greater frequency of PBT than those with low IGA (p = 0.02). There was no relationship between IGA and athletes' PBT when team norms were low (p = 0.48). No interactions for social identity by team norms for prosocial opponent behaviour or antisocial behaviours toward teammates and opponents were found. The findings highlight the potential salient role of group norms to further understand the social identity - moral behaviour relationship in a youth sport setting.Acknowledgments: SSHRC Insight Development Grant (#430-2013-000950)
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".