Managing Conflict and Resistance to Change in a Minor Hockey System
Bibliographic record
Abstract
In March 2017, responding to a pressure to improve athlete development and enjoyment, Hockey Canada moved to change how youth are introduced to hockey by mandating the implementation of a cross-ice development program for its entry-level participants. The mandate of cross-ice programming was to ensure that all 75,000 entry-level participants received increased touches of the puck on an appropriately sized playing surface; thus, heightening their spatial awareness and foundational skills necessary to enjoyably move forward in hockey. As is common for many sport organizations, the proposed programming changes were met with resistance by some stakeholders. Surprisingly, the resistance to the programming changes evolved into a much-publicized intergroup conflict within Hockey Canada’s largest market. The dispute could not be resolved in time for the beginning of the 2017–2018 season. As a result, the defiant local leagues were granted a one-year reprieve from implementing cross-ice programming. With only a one-year reprieve granted, Hockey Canada must now determine the appropriate steps to fully implement their desired programming change and ensure that resistance-based conflicts are limited in the future.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.013 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".