A critical examination of how experiences shape board governance at the community level of sport
Bibliographic record
Abstract
The purpose of this research is to critically examine how individual board members’ behaviours and experiences shape board governance at the community level of sport. To serve the purpose, a qualitative ethnographic approach was appropriate. For one sport, six boards, across one province in Canada comprised the sample. Importantly, these boards govern sport clubs that serve thousands of sport participants in the community area. Observations during monthly/bi-monthly board meetings took place for 1 year with each board, alongside interviews with 30 board members. Data analysis was guided by an interpretative approach to thematic analysis underpinned by concepts related to design archetypes and organizational culture to search for patterns of meaning across the qualitative dataset. The findings illustrate how interrelated levels of culture influenced how board members engage in operational versus strategic governance priorities. Moreover, individual assumptions manifested in varying foci of sense of community (fragmented or cohesive) and the enactment of individual values influenced structural coherence and power. In turn, these assumptions and values shaped board member decision-making as well as board member contributions to board discussions. This study emphasizes how individual board members shape group-level criteria for effectiveness, principles of organizing and domain at the community level of 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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".