How Canadian hockey league general managers build and sustain a culture of excellence
Bibliographic record
Abstract
Leaders within high-performing sport contexts are under increasing amounts of pressure to build a successful (winning) program. This is due to many factors, especially considering the increased financial rewards associated with achieving success in high-performance sport. In addition, professional sport coaches and executives are being increasingly scrutinized by their respective national sport councils, governing bodies, club owners, the media, the public, and fans. To date, most research in this domain has focused on coaches and athletes, which does not take into account teams that have an individual with a greater role, the General Manager. Few positions in sport are as multifaceted and demanding as the General Manager. The purpose of the current study was to gain insight into how hockey General Managers created and sustained a culture of excellence. Semi-structured interviews were conducted with five experienced Canadian Hockey League General Managers who took over poor programs and turned them all around, including leading teams to three Memorial Cups wins, as well as eight League Championships. The interviews were transcribed verbatim and the data was organized into themes and analyzed using thematic analysis.The analysis of the data revealed that the General Managers played a key role in creating culture transformation. The General Managers enacted this cultural transformation by implementing a set of values and principles that governed all organizational decisions, were focused on holistic development, and maintained a level of excellence within every aspect of the organization. Additionally, the findings from this study could provide valuable information for current and future leaders in both high performing sport and business domains by providing crucial knowledge on how to build and sustain a culture of excellence
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".