Bibliographic record
Abstract
Menopause marks the end of menstruation, once generally accepted as the closure of women's reproductive lives. The current medical view of menopause, however, is as a pathological event with its own distinct set of symptoms and diseases. Researchers have described women as facing a dramatic increase in the risk of heart disease, osteoporosis, stroke, and Alzheimer's, all as the result of the impact of changing hormone levels, particularly the decline in estrogen. The clinical literature has interpreted these findings in terms of the absolute necessity of replacing these lost hormones for all women who are menopausal regardless of any other physiological, social, or cultural characteristic they might possess. Using research done in Japan, Canada, and the United States, this paper challenges the notion of a universal menopause by showing that both the symptoms reported at menopause and the post-menopause disease profiles vary from one study population to the next. For most of the symptoms commonly associated with menopause in the medical literature, rates are much lower for Japanese women than for women in the United States and Canada, although they are comparable to rates reported from studies in Thailand and China. Mortality and morbidity data from these same societies are used to show that post-menopausal women are also not equally at risk for heart disease, breast cancer, or osteoporosis. Rather than universality, the paper suggests that it is important to think in terms of "local biologies", which reflect the very different social and physical conditions of women's lives from one society to another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".