“He speaks great English—For a guy from Moscow”: Language ideologies in NHL media
Bibliographic record
Abstract
Ideologies about languages and countries are hard to shake, even in a multinational, multilingual setting like the National Hockey League (NHL) and the journalists who report on it. Despite its historical roots in Montréal and the dominance of Canadian and European players, the lingua franca of the NHL is English. In this work, we used qualitative analyses to examine players’, journalists’, and coaches’ attitudes toward languages other than English used on the ice. Across all groups, we found that Russian speakers were most likely to be assessed negatively, from being taciturn and unwilling to be interviewed (Frederickson, 2023) to being unlikely to speak good English (Keefe, 2023). Additionally, English-speaking players were more likely to associate positive sentiments with native North American English, Swedish, and Finnish speakers, but negative sentiments about Canadian French speakers and players from eastern European countries. Players and coaches also tended to be split on whether it was acceptable for other languages to be spoken in the locker room and on the ice. This work points to a fragmented in-group view of the acceptable language to use in professional hockey.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.021 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".