What is the ‘i’ in team?: exploring individual role identity in sport board governance
Bibliographic record
Abstract
Our study explored regional volunteer sport board member role identities relative to performance regimes and power relations. Specifically, we explored the intersection between board members’ role identities (conscious and unconscious) and power relations and how they shaped the adoption or resistance of performance regimes. Guided by a critical ethnographic perspective, data were collected by observing 58 board meetings and 30 semi-structured interviews. Key findings illustrated how board members with similar role identities exercised the most power on their board and directed how they subscribed to performance regimes, creating in-groups and out-groups on boards based on conscious and unconscious role identities. Further, board members’ relationships with others within the provincial governing body (i.e. volunteer or employee) were influenced by their role identities and, in turn, their compliance with or resistance to performance regimes. Finally, role identities and power relations defined board members’ criteria for success in performance regimes, which occurred both in board in-groups and individually. This study makes a theoretical contribution to sport studies literature by applying role identities theory to understand who exercises power on volunteer sport boards and how the adoption or resistance of performance regimes was shaped by identities and power relations in sport board governance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".