Bibliographic record
Abstract
I have endeavored to include all the important decisions and pivotal moments in the history of the Montreal Olympic Organizing Committee and to correct the errors of fact and interpretation that have routinely been reported about that organization in the media.Responsibility for the text is mine, but the book could not have been anybody's without extraordinary help from the best people I ever worked with.Michel Guay, vice president of operations and sports at the Montreal Olympic Organizing Committee, and I, head of planning, started talking about writing this book in 1973, but each had more pressing business elsewhere.Michel has been a steadfastly close reader, able to provide critical analyses leading to vast rewrites without ever letting me give up.Rolland Gingras worked with me on several projects over the last thirty years, including the Montreal Olympics, advised me on how to tell the story, and corrected my lapses of memory.Yves Morin commissioned the work.His had been the most extraordinary challenge as the financial man of the Montreal Olympics.Even while his health was ravaged by cancer he commented on this book section by section.He died in December 2005.François Godbout, chief legal counsel for the Organizing Committee and before that a contender in Wimbledon and Davis Cup tennis, made many corrections and provided new information on some points
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.312 | 0.229 |
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".