Women Workers Confront One-Eyed Science: Building Alliances to Improve Women's Occupational Health
Bibliographic record
Abstract
Women suffer many health problems related to their work, but attempts to improve their situation face obstacles at two levels: recognition of their problems and ability to organize to prevent them. Recognition by occupational health specialists has been delayed due in part to: A perception that women's issues have been included in research focussed on male workers; pressure to deal with more visible issues of mortality and well-established illness; ignorance of women's working conditions; methodological biases and inadequacies. Recognition by unions is slowed when women and their concerns are absent from union membership and/or governing structures. Feminist health advocates have not often participated in these struggles, due to social class differences and difficulties in linking with some male-dominated unions. Also, due to the wide variety of hazardous working conditions, they do not emerge from population-based analyses of health determinants in the same way as do domestic violence, tobacco or poverty. The authors describe three alliances necessary for successful research, policy and practice in women's occupational health: between feminist and working-class organizations; between feminists and occupational health scientists; between researchers and women workers.
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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".