The influence of social identity on self-worth, commitment, and effort in school-based youth sport
Bibliographic record
Abstract
The current study examined the influence of social identity for individual perceptions of self-worth, commitment, and effort in school-based youth athletes. Using a prospective research design, 303 athletes (Mage = 14.89, SD = 1.77; 133 female) from 27 sport teams completed questionnaires at 2 time points (T1 – demographics, social identity; T2 – self-worth, commitment, effort) during an athletic season. Multilevel analyses indicated that at the individual level, the social identity dimension of in-group ties (IGT) predicted commitment (b = 0.12, P = .006) and perceived effort (b = 0.14, P = .008), whereas in-group affect (IGA) predicted commitment (b = 0.25, P = .001) and self-worth (b = 2.62, P = .006). At the team level, means for IGT predicted commitment (b = 0.31, P < .001) and self-worth (b = 4.76, P = .024). Overall, social identity accounted for variance at both levels, ranging from 4% (self-worth) to 15% (commitment). Identifying with a group to a greater extent was found to predict athlete perceptions of self-worth, commitment, and effort. More specifically, at the individual level, IGT predicted commitment and effort, and IGA predicted commitment and self-worth. At the team level, IGT predicted commitment and self-worth.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".