Advocating for Diversity, Equity, and Inclusion: A Study of the NHL’s #HockeyIsForEveryone Campaign on Twitter
Bibliographic record
Abstract
With more than 6.8 million followers on Twitter (as of Feb. 2023), the National Hockey League (NHL) has a large platform that has the potential to influence societal change not only in the United States and Canada, but also globally. This study aims to understand how corporate social responsibility initiatives pair with diversity, equity, and inclusion (DEI) programs. Furthermore, this research seeks to understand how organizations communicate these messages with their publics through social media. This study specifically examines news frames and charity support behaviors implemented by the NHL in messages about its DEI campaign, “Hockey Is For Everyone,” and analyze fans’ reactions to understand which posts either resonate well or spark backlash. The results of the study aim to provide insightful data for the NHL, as well as other professional sports leagues and organizations, in how to approach corporate social responsibility campaign messaging that is received with positive reactions online.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".