How descriptive norms and social identity interact in predicting antisocial ingroup behaviours
Bibliographic record
Abstract
When membership in a particular group is psychologically salient, individuals are motivated to engage in behaviours that reinforce their identity in that group (Terry et al., 1999). This suggests that athletes who strongly identify with their team may be prone to derogate and chastise teammates in groups where ingroup antisocial behaviours are normative. The current research tested the hypothesis that both exclusionary social norms and antisocial practice norms would moderate the relationship between social identity and ingroup antisocial behaviours. Participants (N = 213, all female) were members of flag football teams (k = 13), all of whom completed measures assessing social identity (ingroup ties, cognitive centrality, ingroup affect), exclusionary social norms, antisocial practice norms, and ingroup antisocial behaviours. A series of moderated multiple regressions revealed that the dimensions of social identity interacted with both types of norms in predicting ingroup antisocial behaviours (ps < .03). At lower levels of exclusionary social norms, social identity was not significantly related to ingroup antisocial behaviours. At lower levels of antisocial practice norms, social identity was negatively related to ingroup antisocial behaviours. At higher levels of both types of norms, however, social identity generally emerged as a positive predictor of ingroup antisocial behaviours. These findings build upon work that examined the mechanisms through which social identities influence morally relevant behaviours in sport (Bruner et al., 2014). Drawing attention to the moderating role of descriptive norms provides insight into the circumstances in which a stronger social identity may actually promote ingroup antisocial behaviours.Acknowledgments: Social Sciences and Humanities Research Council of Canada
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".