A Nordic Ski Coach’s Learning Journey Towards Creating More Inclusive and Safer Sport
Bibliographic record
Abstract
Competitive youth sport coaches who aim to foster inclusive and safer sport often face challenges from their peers, their organisations, and the sociocultural systems in their contexts. A personal learning coach may support coaches’ critical awareness, reflection, and readiness for working towards changing their youth sport contexts. This study details a 15-month collaboration, as Sara acted as a personal learning coach to support Sophie’s critical praxis as they reflected on social issues and experienced shifts in their coaching towards creating more inclusive and safer sport. Grounded in a narrative inquiry methodology, two virtual interviews and 11 virtual meetings occurred. Sara and Sophie also shared reflections through messages and voice notes and one in-person meeting during one of Sophie’s training sessions. Through time-hopping snapshot vignettes, Sophie’s learning journey is presented as they attempt to figure out what to fight for, grow through discomforts and unknowns, and experience progress. Sophie believed that their “mind shifted” towards becoming a “better coach” throughout the collaboration, developing their critical consciousness to change oppressive social conditions in sport. By sharing insights from the collaboration, the study provides vivid examples of the steps coaches and sport stakeholders can take to become more confident in enacting positive change in sport.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".