Puckstruck: Distracted, Delighted and Distressed by Canada's Hockey Obsession
Bibliographic record
Abstract
Like many a Canadian kid, Stephen Smith was up on skates first thing as a boy, out in the weather chasing a puck and the promise of an NHL career. Back indoors after that didn't quite work out, he turned to the bookshelf. That's where, without entirely meaning to, he ended up reading all the hockey books. There was Crunch and Boom Boom, Slashing! and High Stick; there was Max Bentley: Hockey's Dipsy-Doodle Dandy, Blue Line Murder, and Nagano, a Czech hockey opera. There was Blood on the Ice, Cracked Ice, Fire On Ice, Power On Ice, Cowboy On Ice, and Steel On Ice. In Puckstruck, Smith chronicles his wide-eyed and sometimes wincing wander through hockey's literature, language, and culture, weighing its excitement and unbridled joy against its costs and vexing brutality. In exploring his own lifelong love of the game, hoping to surprise some sense out of it, he sifts hockey's narratives in search of hockey's heart, what it means and why it should distress us even as we celebrate its glories. On a journey to discover what the game might have to say about who we are as Canadians, he seeks to answer some of its essential riddles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.070 | 0.025 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".