Considerations for Designing Indigenous Coach Education
Bibliographic record
Abstract
This study used a participatory action research approach to explore the criteria for collaboratively designing culturally relevant Indigenous coach education with Indigenous sport stakeholders from Nova Scotia, Canada. Fourteen Mi’kmaw sport stakeholders, including six coaches (three men and three women), seven administrators (five men and two women), and one Elder (man), participated in the study through methods including online semistructured interviews, unstructured interviews, and focus group discussions to explore their perspectives of how to develop culturally relevant Indigenous coach education. The findings suggest the purpose of designing culturally relevant Indigenous coach education is to enhance cultural pride and support Indigenous coach development. Participants believed these objectives could be fulfilled by addressing topics such as Mi’kmaq culture and history, as well as colonialism. The preferred methods of delivering content included facilitating experiences, storytelling, and mentoring. The findings are interpreted relative to the Calls to Action advanced by the Truth and Reconciliation Commission of Canada, as well as the existing literature on Indigenous coaching and learning. Finally, the Mi’kmaq framework of two-eyed seeing is used to advocate for the bridging of Indigenous and Western perspectives, as a means of decolonizing coach education.
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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 | 0.056 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".