(Re)conceptualizing cohesion: A theoretical realignment and roadmap for future research
Bibliographic record
Abstract
Cohesion, or the unity of a group, has received substantial attention in popular media and from researchers across several contexts. Over the past 40 years, researchers interested in understanding and investigating cohesion in sport have relied predominantly on the work of Carron and colleagues (1985) who outlined their conceptual foundation for the construct and developed the Group Environment Questionnaire. Although their conceptual framework and measurement tool have been useful to gain an understanding of relationships between cohesion and its potential antecedents and outcomes, a closer inspection of its theoretical bases and concomitant definitions provides an opportunity for improved theorizing. In the present review, we revisit the work of Carron and colleagues (1985), the construct of cohesion itself, the state of the literature, and apply a critical eye toward the role that cohesion plays in contributing to a group's dynamics and functioning within sport. In doing so, we identify several concerns with the current state of knowledge regarding cohesion and offer a series of propositions to define, conceptualize (two-dimensional model of cohesion consisting of task and social integration), and more effectively link key inputs contributing to the unity of the group (i.e., interdependence, social identity, teamwork). We believe that there is a pressing need to (re)conceptualize the construct and have proposed a theoretical model for cohesion, as well as a future research agenda, that we hope will reinvigorate research on cohesion in sport and beyond.
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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 | 0.046 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.045 |
| Scholarly communication | 0.017 | 0.064 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.008 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".