Women's Health Surveillance: Implications for Policy
Bibliographic record
Abstract
The previous articles in this supplement provide valuable data and insights about women's health in Canada but also point to significant gaps in information gathering about women's health and about gender differences in health. These gaps are evident in health surveillance activities and in areas of biomedical and social research. As well, the gender implications of social and economic policies are rarely considered in a systematic and consistent way. This long-standing situation is the result of assumptions and values underlying theoretical and practical approaches to data collection, research, analysis and policy development, which have tended to reinforce the centrality of women's reproductive and caregiving roles and ignore or underplay women's experiences in other sectors of social life. [ 1 – 4 ]
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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 | 0.071 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".