SKATING TOWARDS EQUITY: A NEW ERA OF DIVERSITY AND INCLUSION IN THE NHL
Bibliographic record
Abstract
This research examines the ability of the NHL to foster meaningful culture change, particularly through the implementation of diversity, equity, and inclusion (DEI). It delves deep into hockey’s ethos, evaluating how the NHL is integrating these critical values amidst evolving societal expectations. This exploration delves into the multifaceted role of sports as both a reflection of societal dilemmas and a catalyst for profound transformation. \n \nEmerging from 23 interviews, this study identifies significant societal events—the presidency of Barack Obama, the polarizing elections of 2016 and 2019, the global upheaval caused by the COVID-19 pandemic, and the renewed focus on racial injustice following George Floyd’s death—as key drivers reshaping DEI strategies in major sports leagues. These events challenged the sports domain to confront its exclusionary legacies, navigate socio-political shifts and commit to a future that is genuinely inclusive and equitable. \n \nThis research underscores an urgent need for transformative actions within hockey. Actions that not only challenge but disrupt the status quo. My exploration reveals that the NHL’s success in embedding DEI and transforming its culture hinges on a comprehensive strategy that addresses multiple factors at various systemic levels. It highlights the imperative for a coordinated, evolving approach to ensure DEI becomes a permanent facet of the league’s identity and operational ethos.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.045 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".