Bibliographic record
Abstract
A ccording to baseball great Yogi Berra, watching teammates Mickey Mantle and Roger Maris hit back-to-back home runs, game after game, for the New York Yankees in the early 1960s was like déjà vu all over again.The phrase -one of many "Yogiisms" that led The Economist to recognize Berra, in 2005, as the "Wisest Fool of the Past 50 Years"springs to mind as I read Ryan O'Connor's The First Green Wave -not because this story has been told before -it has not -but because so much of this book deals with times, places, and events that I recall. 1 Pollution was in the air when I began my academic career in 1968.Late that year, as I settled in to my MA program at the University of Toronto, the release of the Hall Commission report on The Air of Death, a television program first aired on CBC in 1967, re-animated discussion about the accuracy of the documentary's claims.As debate raged over Hall's critical findings, Larry Gosnell and Stanley Burke, the producer and narrator of the controversial film, showed their work on the university campus.Once again, viewers saw images of black smoke belching from an industrial plant and heard the grave, familiar voice of CBC television's national news anchorman intone: "Every day your lungs inhale fifteen thousand quarts of air and poison."Continuing, Burke drove home the frightening message: "You breathe sulphur dioxide, which erodes stone.Benzopyrene makes cancer.Carbon monoxide impairs the mind … Death has been gathering in the air of every Canadian city.Poisons continue to accumulate and you must keep breathing."2 xiv The First Green Wave
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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 | 0.007 |
| 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 | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.767 | 0.764 |
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".