Ethical thinking in occupational and environmental medicine: Commentaries from the Selikoff Fund for Occupational and Environmental Cancer Research
Bibliographic record
Abstract
A tribute to Dr. Irving J. Selikoff MD, the founder of this journal, is indeed welcome now more than two decades after his passing. He was known during his lifetime as the US Father of Environmental Medicine which at the time encompassed occupational medicine and much more as industry also polluted the general environment. The 1970s were a busy time as OSHA and the EPA were newly formed and high exposures to workers were no exception. Dr. Selikoff was a brave pioneer examining workers throughout the country and Canada, publicizing their exposures, and writing and presenting the scientific results. Industry was not always receptive and controlled an astounding amount of narrative, with the creation of the American Journal of Industrial Medicine filling a void of scientific need. We four authors write about the ethics of occupational health, the plight of nuclear energy workers, the climate crisis and opportunity for unions to engage workers, and the global march toward educating medical students on workers' health and safety. All four of us interacted with Dr. Selikoff during his tenure at Mount Sinai, and over the years joined each other in promoting his legacy. Toward that end we have written articles honoring his memory.
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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 | 0.067 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.066 | 0.090 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".