Regional Policy in Multi- Level Sport Governance in Yukon, the Northwest Territories, and Nunavut: History and Implications
Bibliographic record
Abstract
Much contemporary sport policy is focused on policy at the national (Parent et al., 2018;) and municipal (Oncescu & Fortune, 2022) levels, or in relation to community sport organizations (Rich & Misener, 2019). As such, Rich and colleagues (2023) identified a gap concerning the significance of regional actors and their implications on both institutional arrangements and policy implementation within Canadian sport governance literature. Further, very little research has examined sport policy within the Territorial North. The exploration of these gaps can play a significant role in understanding policy implementation and the agency of sport policy makers at regional levels within multi-level sport governance systems in Canada. Therefore, as part of a broader national study to explore the involvement of regional policy in sports institutions and comprehend how it affects organizational fields within multi-level sport governance systems, we collected and analyzed over 150 documents related regional policy from the Yukon, Nunavut, and the Northwest Territories (NWT). In this session, we will present a critical analysis of relevant sport policy implementation in the Yukon, NWT, and Nunavut between 2002 and 2023. Our findings contribute to discussions concerning regional sport governance within the Canadian context, and specifically highlights the intersection of sport and territorial policy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".