A Collaboration Model for Knowledge Transfer from Sport Science to High Performance Canadian Interuniversity Coaches
Bibliographic record
Abstract
This study examined the extent to which improved collaboration between sport scientists and coaches of high performance athletes might improve knowledge transfer in sport. The research includes a review of the extant literature on collaboration to develop a model of successful collaborative practice. The model is then empirically tested to determine whether such a model can improve our knowledge of the mechanisms for effective knowledge transfer in sport. To accomplish our purpose, we interviewed 38 high performance coaches employed in a variety of university settings and from a variety of sports to determine the factors that inhibit and facilitate, knowledge transfer. The model was used to guide the data analysis. The results showed that 14 of the coaches interviewed were involved in collaborative relationships with sport scientists and the factors in the model did help to explain why some coaches collaborate while other coaches may not. Factors such as different types of motivation, the personal characteristics of the coach and the structural characteristics within which the coach operates seemed to influence the extent of the collaboration between the sport scientist and the coach and ultimately the effective transfer of sport science knowledge. Sport organizations can apply these findings to improve the effectiveness of knowledge transfer to coaches of high performance athletes.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".