Normative versus transformative coaching approaches: Profiling the social justice activism and allyship of Canadian high school sport coaches
Bibliographic record
Abstract
In Canada, high school sport is a popular activity used to foster the physical and psychosocial development of student-athletes. Despite the popularity of high school sport, social justice inequities (e.g., racism, ableism) continue to shape student-athletes’ experiences. Given that the mission of School Sport Canada (i.e., the national governing body of school sport) is to facilitate the “total development of student athletes through interscholastic sport”, there is a need to study the way coaches think about and act for social justice. Thus, the purpose of the study was to profile the social justice activism and allyship of Canadian high school coaches. Semi-structured interviews were conducted virtually (i.e., via Zoom) with 28 Canadian high school sport coaches. Interview data were subjected to reflexive thematic analysis, which led to the development of two coach profiles based on the two continua model for life skills teaching (Camiré, Citation2023). The two coaching profiles – the normative coaching approach (i.e., general disinterest in social justice) and the transformative coaching approach (i.e., proactive social justice activism through a developed critical consciousness) – are contrasted across four overarching themes (a) I’m (Not) Confident, I (Don’t) Understand; (b) (Un)intentionally (Re)acting; (c) You See Excuses, I See Obstacles to be Toppled; and (d) (Dis)engaged and (Non)autonomous Education. Findings have practical implications for centering social justice in coach education, for developing critical praxis related to activism and allyship, and for instigating systemic policy changes that can make the Canadian school sport system safer, more equitable, and more inclusive for all participants.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".