Bibliographic record
Abstract
Between such novels as The Apprenticeship of Duddy Kravitz, St. Urbain's Horseman, and Barney's Version, Mordecai Richler pursued his obsession with sports -- and he wrote brilliantly about such sports as ice hockey, baseball, salmon fishing, bodybuilding, and wrestling. His essays and articles appeared in such prominent and diverse places as GQ, Esquire, The New York Times Magazine, Inside Sports, Commentary, and The New York Review of Books.Richler spent time with Pete Rose, Wayne Gretzky, and Gordie Howe (when a reporter asked Gordie's then eighty-year-old father if he was still interested in sex, Howe pere replied, You'll have to ask somebody older than me.) He traveled with Guy LaFleur's Montreal Canadiens, and with the Trail Smoke-Eaters to Stockholm, for the world hockey championships. When a Swedish reporter confronted the Smoke-Eaters' coach and accused him of encouraging violence on the ice, the enraged coach replied, But I condone it absolutely.There are shrewdly perceptive pieces here about Sandy Koufax, Hank Greenberg, and lady umpires, and a marvelous essay on Richler's unlimited enthusiasm for the all-inclusive Encyclopedia of Jews in Sports, which includes among its champions one Steve Allan Hertz, an infielder who played a total of five games in Houston in 1964 and had a batting average of .000.This then is a superb novelist writing unforgettably about his obsession for sports. The work sparkles with Richler's hallmark irony, wit and shrewd perception. It is a book no sports -- or Richler -- fan will want to miss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.411 | 0.215 |
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".