Birds of Different Feathers: Coaches’ Perspectives of Cultural Diversity and Team Dynamics in Professional Sport
Bibliographic record
Abstract
As the number of international transfers increases in professional sport teams, it is unclear how this diversity impacts team functioning, and also, whether coaches feel equipped to meet this new challenge. In this study, we explored professional coaches’ perceptions and experiences of cultural diversity (i.e., national and racioethnic diversity), with a specific emphasis on implications for team dynamics. Semistructured interviews were conducted with nine professional coaches from four interdependent team sports in France (i.e., basketball, football, handball, volleyball). The coaches had experience leading first or second division men’s and/or women’s teams (e.g., two highest national championships in France, Canada, or Italy) and national teams (e.g., France, Belgium, China). Based on a critical realist approach, results indicated that coaches purposefully considered cultural diversity within their teams, and discussed stereotypical differences based on nationalities pertaining to work ethic, communication, and motives for action. Coaches’ international experience and intercultural competence seemed to be key elements that influenced their willingness to consider the cultural diversity of their teams in their coaching/management strategies. Herein, we discuss the importance of purposefully considering a team’s context (e.g., sport, member composition, geographical location) and note that coach intercultural competence appears to be a concept warranting further investigation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".