Overcoming the challenges of coaching misconduct: a case study of the implementation of the responsible coaching movement program in Canada
Bibliographic record
Abstract
Purpose/Rationale Sports organizations face ongoing challenges to ensure safety, including concussion protocols, facility upkeep, and coaching oversight. This research explores the implementation of the Responsible Coaching Movement (RCM) to understand initiatives that maintain institutional integrity amid rising coaching misconduct.Design/Methodology/Approach Through a case study design and the use of institutional work and legitimacy, representatives from 18 national, provincial/territorial, community, and advocacy organizations were interviewed.Findings The results revealed that ceremonial activities and unconscious myths can be used to maintain the legitimacy of the institutional actors by sending the message of a safe sporting environment.Practical Implications The practical implications of this study suggests that the findings can help shape public perceptions of sports organizations and support the development of proactive strategies for managing coaches, ultimately reducing the risk of coaching misconduct.Research Contributions The findings from this research contribute to the sport management and governance literature by advancing the use of legitimacy maintenance theory to examine the impact of safe sport practices in Canada.Originality/Value The RCM’s research moves beyond the coach-athlete relationship by focusing on mechanisms used by governing bodies to ensure the Safe Sport mandate and advancing our understanding of how institutional actors maintain legitimacy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.049 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".