Regional policy and organizational fields in multi-level sport governance
Bibliographic record
Abstract
Research Questions: Broadly, we sought to explore the role of regional policy in sport institutions and understand their implications for organizational fields in multi-level sport governance systems.Our research questions were (1) how do changes in regional policy impact the way that organizational fields are structured within multi-level governance structures?and (2) how does regional policy impact sport policy implementation?Research Method: We used an instrumental case study methodology of regional policy in the Province of Ontario.Data were collected using document analysis.We collected 88 policy documents produced between 1995 and 2021.Data were analyzed using a critical policy analysis approach.Results and Findings: Our findings demonstrate the ways that administrative arrangements and the ideas and beliefs underpinning regional policy had important implications for sport policy implementation in Ontario.The location of sport in successive provincial administrations had implications for the expected role of sport in the province.Ideas and beliefs related to what regional government should do, and who should be responsible for the delivery of services also impacted the way that sport was delivered in the province through the period studied.Implications: Our work examines the agency of regional policymakers in the structuration and change of organization fields in sport institutions.We also critically examine the linkages between organizations in multi-level sport governance.Future work is required to understand the range of regional pressures that impact sport policy implementation in multi-level sport governance systems.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".