Social identification and relational efficacy beliefs in sports teams and training groups
Bibliographic record
Abstract
Sparse research has been conducted to investigate the link between social identification and relational efficacy beliefs, variables consistently associated with performance. The purpose of this study was to investigate the extent to which (1) social identification with one’s team or training group relates to perceptions of self-efficacy, group-focused other-efficacy, and relational inferred self-efficacy (RISE); (2) social identification and self-efficacy are indirectly associated through group-focused other-efficacy and RISE; and (3) assess whether any associations differed for team and individual sport athletes. Athletes from a range of individual (n = 98) and team sports (n = 101) participated in the study. Results from structural equation modelling demonstrated that social identification was significantly related to RISE (β = .51) and other-efficacy (β = .60) for all athletes. Social identification was also significantly related to self-efficacy (β = .40) for individual sport athletes only. Support for the indirect role of RISE in the relationship between social identity and self-efficacy (β = .43) for team sports athletes and individual sports athletes (β = .26) was also observed. Overall results demonstrate the unique relationship that social identification has with athlete self-efficacy and RISE for individual and team sports athletes.
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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 | 0.009 |
| 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 | 0.000 | 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".