The Sport Partnership Collective: Advancing the science and practice of meaningful research partnerships
Bibliographic record
Abstract
Research partnerships can play a role in translating/mobilizing sport knowledge in practice to improve research access, use, and comprehension. These partnerships are plentiful in sport, and hold potential for real-world impact, yet little is known about how to optimize their effectiveness. The Sport Partnership Collective (SPC) is a group dedicated to advancing research, capacity development, and support for research partnerships in sport. Grounded in principles of meaningful engagement, the SPC promotes partners' awareness, reflection, and actioning of strategies to foster positive partnership experiences that highlight shared decision-making, knowledge exchange, and co-production (Hoesktra et al., 2021). The objectives of this presentation are to explore the value of meaningful research partnerships, describe the SPC's application of meaningful engagement strategies, and propose actionable steps to develop and support meaningful research partnership science and practice. Through co-delivery by a researcher and knowledge user within the SPC, the presentation will first highlight the potential of sport research partnerships for driving advancements in research and practice, challenges in establishing effective partnerships, and the need for capacity support within the sport sector. Then, examples will be shared of how the SPC leveraged meaningful engagement principles to improve their own collaboration, knowledge exchange, and real-world impact. Finally, a co-generated agenda will be proposed which calls for sport sector development and support in research partnership educational opportunities, connection activities, and resource/toolkit creation. By offering an inside look at partnership development, this presentation provides practical insights and critical next steps to advance sport partnership research and practice.
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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 | 0.202 | 0.189 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.024 | 0.055 |
| Scholarly communication | 0.053 | 0.065 |
| Open science | 0.007 | 0.064 |
| Research integrity | 0.030 | 0.038 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".