The process of implementing a multi-level and multi-sectoral national sport policy: cautionary lessons from the inside
Bibliographic record
Abstract
The purpose of this paper was to (1) present and critically reflect upon a national sport policy’s implementation and monitoring process in a multi-level, multi-sectoral context from an insider’s perspective and (2) provide recommendations for future research and policymakers regarding sport policy implementation and monitoring. Based on hundreds of documents (e.g. formal and personal meeting notes, formative and summative evaluation reports) gathered over the policy’s lifespan, the paper critically reflects on the second Canadian Sport Policy’s (CSP) implementation process between 2012 and 2022, which comprised grassroots, high performance, and sport for development goals, using the multiple governance framework. The reflection highlights key challenges for implementing a soft (national sport) policy in a complex, multi-level, multi-sectoral governance context, such as the normative soft policy seeing a ‘policy for all’ becoming a ‘policy for no one’, no stakeholder accountability per se, nor power for the policy intermediary to enforce implementation. This resulted in the CSP 2012’s ceremonial attribution of success because any action could be seen as fitting within policy goals. The paper highlights the importance of (1) aligning policy development, implementation, and evaluation between macro and micro levels; (2) a more holistic policy implementation process analysis using in situ methods; (3) understanding the personal experiences, struggles, and tensions found within policy implementation to explain potential outcomes; (4) policy ambiguity and equifinality limiting policy implementation evaluation; (5) resources/dedicated funding as a policy implementation success driver; and (6) potential tools (e.g. use of outside experts, conceptual maps) for soft policy implementers/monitors and researchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".