Bibliographic record
Abstract
While only a single name appears on the cover, the effort that went into producing this book was a collaborative one.It is the work of friends and family, colleagues and strangers, archivists and librarians.And I want to acknowledge and give thanks for their many and varied contributions.The most appropriate place to start is to thank the people who shared with me their experiences as spectators at Maple Leaf Gardens in the 1930s.Twenty-one people set aside time in their days and, in many instances, opened their homes to me.We met in Toronto, Kingston, Waterloo, Calgary, and Vancouver.My hope is that they will feel that I have done justice to their memories and accurately interpreted their experiences as hockey spectators.In some cases, I met these charismatic and enthusiastic former spectators through the efforts of administrators at assisted-living facilities, and I am grateful for their help.A number of employees of Maple Leaf Sports and Entertainment, the corporation whose holdings include the Toronto Maple Leafs hockey club (and once included Maple Leaf Gardens) were very helpful, including Paul Beirne, who connected me with long-time Leafs' ticket subscribers.Absolutely invaluable assistance was also provided by Donna Henderson and her staff at the then-Air Canada Centre ticket office.It was Donna who alerted me to the existence of the Maple Leaf Gardens subscriber ledgers that comprise such a significant portion of the evidence upon which chapter 3 is based.Like many historians, I relied heavily on archival collections.And, given the scarcity of accounts of spectator experiences, I combed through as many different archives as I could access.In all cases, the archivists at these facilities were generous with their time and knowledge.I am especially grateful to the reading room staff at the Archives of Ontario, the City of Toronto Archives, the National Archives in Ottawa, and the Centre for Canadian Architecture in Montreal, in particular Howard
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.421 | 0.345 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".