Implementing biomechanical analyses for indigenous athlete development: Lessons from applying the Knowledge-to-Action framework with a remote First Nation hockey team
Bibliographic record
Abstract
Biomechanical analyses can greatly enhance athlete development by enabling coaches to create tailored training programs for individual athletes and teams. Knowledge Translation (KT)-the process of developing and implementing research-facilitates the integration of these programs through community partnerships. This paper explores how KT of emerging sports science technologies can support Indigenous athletes and shares lessons from applying the Knowledge-to-Action (KTA) framework with coaches from a remote First Nation hockey team in northern Québec, Canada. Involving ten U18 male athletes and four coaches, the KTA process assisted in creating an athlete testing program that included analyses of skating and shooting techniques. While effective, the KTA process highlighted the need for additional tools to navigate complex dynamics in Indigenous research translation contexts. We recommend that researchers consider frameworks like Indigenous Science, Technology, and Society (Indigenous STS), which prioritize Indigenous expertise and governance in research collaborations, ensuring that Indigenous peoples can develop and use sciences and technologies on their own terms.
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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 | 0.066 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.024 | 0.031 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".