Bibliographic record
Abstract
I lived in Toronto for six months and consumed as much hockey as possible. I spent most of the time in various hockey bars and at Ricoh Coliseum where the Toronto Marlies play their games. My first intention was to get inside a hockey club, but since I soon would learn that my ambitions was a bit over my head I settled for presence in the large hockey community that exists in Toronto. I could literally find people that were open and interested to talk about hockey everywhere. \n\nThe focus in this thesis is hence the social aspects of consuming hockey, whether it´s \nwatching hockey games in a bar, or at the arena. The social drama at hockey games is one particular interest. For instance I discovered a form of nonverbal communication that exists between players and spectators. This communicative experience is usually created through actions or situations that happen between players on the ice. Take the fighting for instance. In every game I watched were a fight occurred, the crowd become louder and clearly paid attention to what happen on the ice. A sure sign of a social commitment, a hidden bond or something similar, which makes it all more intense. Furthermore, hockey games seems to create an opportunity for a special atmosphere that separates it from other parts of social life. A prove of a liminal stage with losses of structure where \npeoples can experience something they might not be able to elsewhere (Turner 1988). This paper seeks to understand why hockey is relevant, and why it’s moving, provoking and engaging big \ncrowds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".