Researcher’s Reflection: Learning About Menstruation Across Time and Culture
Bibliographic record
Abstract
Abstract Mendlinger looks at the ethnically pluralistic society of Israel to explore how young women acquire the knowledge informing their health behaviors including those related to menstruation. Beginning with the origin story of her research agenda at a time of mass immigration to Israel, she then offers the main findings from 48 in-depth interviews with mothers and daughters that fall into several categories of mother-and-daughter dyads: native-born Israelis and those composed of immigrants from North Africa, Europe, the Former Soviet Union (FSU), United States or Canada, and Ethiopia, each bringing traditional knowledge and practices to bear on what it means to menstruate. Mendlinger’s work, anchored by the voices of women, vividly demonstrates that four types of knowledge: traditional, embodied, technical, and authoritative that are passed generationally from mother to daughter change through the immigration process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".