Reflections on investigating sport governance processes
Bibliographic record
Abstract
Sport governance research has demonstrated the use of different qualitative methodological approaches to investigate governance processes. To build off this work, this study provides reflections on investigating governance processes in non-profit sport organization boards via a multi-method, in situ, and longitudinal approach. Using an autoethnography, a diary serves as the source of data to present first-hand reflections. Reflections are based on the lived experiences of investigating governance processes via 79 hours of overt non-participant observations of board meetings, 18 semi-structured interviews, and over 1,000 documents. Reflections are discussed as strengths (i.e. the cruciality of observing the phenomenon, the value of multiple methods, and technology’s ease) and challenges (i.e. participant recruitment and overwhelming demands). This study is warranted to inform sport governance scholars about methodological learnings to investigate governance processes. Reflections, thus, inform future sport governance research to understand the nuances of undertaking a multi-method, in situ, and longitudinal approach. Collectively, learnings offer implications for sport governance researchers, thereby advocating for the advancement of novel methodological approaches.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".