"Who's Watching Anyway?" & Other Gender Exclusionary Narratives of Canadian Hockey Digital Discourse
Bibliographic record
Abstract
This research works to understand the origins and effects of Instagram users'hostile language choices within online hockey discourse. Findings of previous studies suggest the existence of distinct hostility towards female athletes within online sports discourse. Discourse which is disproportionately hostile towards female athletes may alienate women from participation in the sport and fan culture. This study is an exploration of hostile language choices and exclusionary narratives directed towards women's participation in the sport of hockey, in a Canadian context. Hockey Night in Canada is a renowned site of Canadian hockey discourse. For this reason, data collected from the Hockey Night in Canada Instagram page will be used to examine the differing language choices users make when describing female hockey teams and players comparatively to when describing male hockey teams and players. The findings of this data, paired with Turner's (1987) theory of self-categorization and Marx's (1844) theory of alienation, work to suggest potential causes and consequences of exclusionary digital discourse that may affect women's participation in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".