Bibliographic record
Abstract
This paper examines in-depth the recent argument popularized by Brazilian gynecologist, Elsimar Coutinho and colleagues, that regular menstruation in is an unhealthy and unnecessary process that causes women countless health and emotional problems, and that the most medically advanced treatment for menstruation would be its total cessation in all women of reproductive age. The author explores the long history of medical views of menstruation which are often informed by the notion that menstruation is an ailment, or a disorder that requires a medical intervention. The author compares this view with the most recent research on menstruation by evolutionary biologists, such as Margie Profet, and anthropologists Emily Martin and Beverly Strassman, who have, in their own ways, found a variety of health benefits linked to menstruation other than the established link between fertility and the menses. Underlying the author's review of the medical pronouncement on women's natural cycles is the question: Why do women menstruate? Her conclusion indicates that this question has not been adequately addressed in medical and scientific literature, which has sought to explain away or eradicate menstruation. New research needs to assess the value of the regular processes of women's bodies so that can we fully understand their role and function in the physical and emotional health of all women.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".