Bibliographic record
Abstract
When support for Black Lives Matter protests grew throughout 2020, some NHL players spoke out to back the movement and joined Black-led marches across North America. “Listen and learn” became key words for white players showing support. Around the same time, Black professional hockey players created the Hockey Diversity Alliance (HDA) to push for “sustainable change” at various levels of hockey (HDA 2020). However, in October 2020, the HDA announced that it would “operate independent of the NHL” after not receiving any response to its pledge (2020). There is a collection of powerful voices in the NHL which includes players, coaches, managers, and league representatives. However, unlike the WNBA, NBA, MLB, MLS, and NFL, most players, coaches, managers, and representatives are white. As a result, it offers a sample from which to consider the responses of people who identify as white or are of white European settler descent to the calls for justice and support from Black, Indigenous, and People of Colour within and outside of hockey. The purpose of this discussion is to begin to breakdown the tangible work of responding to calls for justice for Black people in North America. Part of this process is the cataloguing of the range of responses. This paper will begin to gather, catalogue, and observe the different responses of white professional hockey players and league representatives to the work of Black Lives Matter organizations and protests and the HDA.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.025 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.027 | 0.015 |
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".