The Golden Age We Have: Who's Knocking It and Why?: Keynote Address to the Seventh Annual NINE Spring Training Conference, March 18, 2000
Bibliographic record
Abstract
Leonard was born in Moscow, Russia, not Idaho, in 1923 and came to the United States to New York City with his family in 1928. New York, as you know, was then, much more than now, a center of America's sport and culture, and Leonard was certainly drawn to it. In 1948, two years after receiving a B.A. from Columbia University, he joined the sports staff at the New York Herald Tribune . In 1954 he moved to the New York Post , and in 1963, to the New York Times . In 1979 he heeded Horace Greeley's advice, moving to Palo Alto, California, as a sports editor and columnist for the Peninsula Times Tribune . He assumed emeritus status in 1984 but continued to write for the Times Tribune and, since 1993, for the Oakland Tribune . Leonard Koppett has long been my favorite sports journalist for the very reasons that he enjoys preeminence in his profession. From 1964 to 1984 I devoured his weekly columns in The Sporting News . In a profession dominated by personal opinion and anecdotal evidence, he approaches his craft as a teacher and an educator. I have read most of his twelve books, four of which have been revised and reissued, and they share the same characteristics.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.129 | 0.059 |
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".