A conceptual analysis of cliques as described by elite level coaches
Bibliographic record
Abstract
The emergence of cliques within teams is an important consideration, and as such, is frequently highlighted in the sport literature (e.g., Eys et al., 2009; Fletcher & Hanton, 2003). Following previous research that assessed the nature of cliques through the perceptions of elite athletes (Martin et al., 2014), the current paper expanded this exploration to individuals who are in a unique position to dictate team structure—coaches. Semi-structured interviews were conducted with 18 elite level coaches (Mage = 37.67, SD = 6.90; five female coaches) who were asked to reflect on what cliques and subgroups meant to them, the processes related to their emergence within teams, and the resultant outcomes. Participants had an average of 13.83 years experience coaching their respective sports (e.g., rugby, basketball, hockey, soccer, swimming, rowing, triathlon), which ranged from Canadian Interuniversity Sport to Professional, to International competition. Results indicated that the term ‘clique’ was largely construed as describing only negative subgroups, whereas the term ‘subgroup’ was not value-laden. Generally, coaches described the potential for cliques/subgroups to influence team and individual outcomes in positive (e.g., supportive friendships, teammate commitment) and negative ways (e.g., conflict, competition, and stress among team members). In addition, the reflections were integrated to form a conceptual framework, revealing the process whereby subgroups emerge and influence group functioning—a process shaped by preceding elements such as member characteristics (e.g., cohorts, skill level) and behaviours (e.g., discipline, social habits), as well as contextual factors (e.g., team size, sport type). Within this process, it was clear that coaches managed—both proactively and reactively—the emergence of and behaviours exhibited by cliques, often relying on athlete leaders for their identification and for the management of the social environment. These findings will be discussed in terms of their theoretical and practical relevance to sport.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".