Epistemic Communities and Knowledge-Based Professional Networks in Sport Policy and Governance: A Case Study of the Canadian Sport for Life Leadership Team
Bibliographic record
Abstract
This investigation examined how a network of knowledge-based professionals—the Canadian Sport for Life Leadership Team (CS4LLT)—as a newly emerging organizational form was able to influence the Canadian sport policy and governance process in an attempt to reshape Canadian sport. The analysis draws upon the epistemic community approach (Haas, 1992; Haas & Adler, 1992) and empirical data collected as part of an in-depth case study examination into the leadership team and senior Sport Canada officials. The findings support the notion that the CS4LLT, as a network of knowledge-based professionals with legitimated and authoritative and policy-relevant expertise (epistemic community), was able to influence the Canadian sport policy process through (i) influencing key governmental actors by (re)framing policy-relevant issues and (ii) establishing knowledge/truth claims surrounding athlete development, which, in turn, enabled direct and indirect involvement in and influence over the sport policy renewal process. More broadly, the study draws attention to the potential role and importance of knowledge-based professional networks as a fluid, dynamic, and responsive approach to organizing and managing sport that can reframe policy debates, insert ideas, and enable policy learning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".