Bibliographic record
Abstract
It may seem incredible now, but up to just 25 years ago there was very limited research specifically targeted at women's health. The world seemed to assume that, except for issues related to reproduction, women's health problems, needs, and solutions were essentially the same as men's.1 As a result of vigorous lobbying by women from all over the world, research on women's health needs mushroomed in less than three decades. Major studies are now generating increasing evidence on important differences between men and women, from the cellular to the societal level.2 Almost by default, the strong emphasis on women's issues (which we applaud and support) has revealed areas of men's health that require just as much attention. Perhaps one of the most puzzling is the difference in life expectancy between men and women. Despite having had most of the social determinants of health in their favour, men have higher mortality rates for all 15 leading causes of death3 and a life expectancy about …
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".