Partnering for Impact: A Blueprint for Knowledge Translation Initiatives in the Canadian Sport Sector
Bibliographic record
Abstract
Evaluation is an essential organizational practice in sport, but many organizations do not have adequate capacity to engage in evaluative work. To address this gap, academic researchers partnered with Canada’s Sport Information Resource Centre, a nationally serving nonprofit dedicated to knowledge translation in sport, to develop, deliver and evaluate a series of webinars and knowledge products (e.g. blog posts, videos) that aimed to build evaluation capacity among sport organizations. The initiative produced four webinars and 16 knowledge products that reached 753 sport stakeholders, with 86% of survey respondents reporting an increase in evaluation knowledge. Using the Knowledge to Action and RE-AIM frameworks, this paper provides a blueprint for higher education professionals seeking to co-develop, co-deliver and co-evaluate knowledge translation initiatives in partnership with nonprofit sport organizations in Canada.
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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 | 0.274 | 0.234 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.045 | 0.052 |
| Scholarly communication | 0.048 | 0.025 |
| Open science | 0.010 | 0.046 |
| Research integrity | 0.016 | 0.028 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".