Don Cherry's Final Rant: Illuminating Canadian nationalism, racial xenophobia, and hegemonic masculinity
Bibliographic record
Abstract
Don Cherry was fired from his position as co-host on the national show “Hockey Night in Canada: Coach’s Corner” in November 2019, following a rant where he singled out new immigrants for not wearing a poppy in support of Remembrance Day. Cherry’s firing was met with fury and outrage by many of his long-time supporters. In this thesis project, I explore these responses in relation to the following broad research question: How does Don Cherry’s final rant on Sportsnet and the popular response to his firing on Twitter, illuminate the continuing salience of white supremacy, xenophobia, hegemonic masculinity and colonialism in Canadian sports discourse? \nDrawing on the fields of feminist, anti-colonial and anti-racist studies, and literature in sport studies I conducted a critical discourse analysis of comments on selected national news reports, posted on Twitter. The overall objective of my project was to question taken-for-granted narratives and ideas of Canadian national identity, and explore the implications of these ideals. Using Canadian hockey culture as a case study, my aim was to develop a rich and accessible entry point for theorizing sports culture and to assess the possibilities and problems associated with re-imagining hockey as a more equitable site of engagement.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.038 | 0.028 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".