Reflections on cohesion research with sport and exercise groups
Bibliographic record
Abstract
Abstract The purpose of this article is to highlight cohesion research emanating from sport and exercise investigations. Across 3 sections, we detail how physical activity cohesion research converges with, and diverges from, mainstream literature in other areas (e.g., organizational psychology). In the first section, information pertaining to the definition, conceptualization, and measurement of cohesion in sport and exercise is provided and contrasted with recent reviews on these topics from organizational psychology. The second section provides an overview of recent studies conducted within the physical activity context to illustrate the diverse nature of cohesion research in this area. Specifically, the summary of sport literature highlights the associations of personal, social, and team factors to cohesion, in addition to drawing attention to its potential maladaptive effects. Exercise research is summarized via associations with personal, leadership, environmental, and group factors. Finally, in the third section, future suggestions are provided encouraging researchers to explore (a) the temporal dynamics of cohesion, (b) greater theoretical integration, (c) measurement issues, and (d) diverse populations of physical activity participants.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".