Intra- and International Similarities and Differences between Sport Teams and Exercise Classes: Canada and Germany in Contemplation of a Social Identity Approach
Bibliographic record
Abstract
To date, a research gap is existing – or may have been overlooked in past group dynamic research (Bruner, Eys, Beauchamp, & Côté, 2013), whether consisting differences within components of a social identity approach can be revealed between PA collectives, and further, may predict adherence of sport team and exercise class members, respectively (Lindahl, Stenling, Lindwall, & Colliandera, 2015). As sustainable methods of maintaining participation rates are key to becoming a healthy and active population (Beauchamp, 2019; Bruner & Martin, 2019), this project is aiming to understand a range of psychosocial variables around which people develop salient identities (e.g., social, athletic, and exercise identities). Evaluating the generalizability of a social identity approach regarding individuals’ PA adherence in Canada and Germany will be beneficial for the current research field as well as practitioners. The investigation should provide transnational insights and further recommendations for practitioners how to promote participation and handle adherence. Therefore, the goal of this study is to determine social identity and self-categorization effects which support extensive PA behavior (i.e., adherence) in sport teams and exercise classes, respectively.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".