“I Used to Love Scheifele:” Dominant Narratives on Reddit About the Canadian Division of the Stanley Cup Playoffs
Bibliographic record
Abstract
The community-moderated content aggregation social media site Reddit has emerged as a popular destination for discussion of topics of interest in news, sport, and entertainment. This study explored the discourse of hockey fans during the playoff rounds of the Canadian Division in the 2021 Stanley Cup playoffs. The study analyzed fan discourse around two incidents which garnered significant media attention, and subsequent Reddit chatter—the inadvertent knee-on-head collision that knocked Toronto Maple Leafs’ captain John Tavares out of Round 1 and the deliberate late hit by Mark Scheifele of the Winnipeg Jets, leading to his suspension for the remainder of Round 2. On Reddit, both incidents revealed a community of hockey fans eager to engage in spirited, sometimes profane, discussion. Very quickly, discussion of the incidents led to the creation of a dominant narrative on Reddit, with the architecture of the site itself helping to inhibit alternative points of view. This contradicts the popular view of Reddit as a fan-powered community because of affordances which inhibit dissent and reward repeating other debate participants. This instant in-group creation could ultimately act as a barrier to fandom among Reddit’s 850 million users not as passionate about particular narratives of 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.019 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| 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".