A THEORETICAL PROPOSAL FOR LATE LUTEAL PHASE BEHAVIOURAL CHANGES IN AN EVOLUTIONARY CONTEXT
Bibliographic record
Abstract
Late luteal phase behavioural changes (LLBC) have been reported for humans and some other primates, and encompass a wide spectrum of behaviours. Several studies have estimated that a majority of women manifest some LLBC. At a more extreme end of the spectrum, in a small minority of women, LLBC have been classified as major emotional disturbances or medical conditions (although not without controversy), for example, as part of premenstrual dysphoric disorder. In this report, an attempt is made to place LLBC in an evolutionary context; some of these behaviours, in particular at a less extreme end of the spectrum, may have co-evolved with reproductive physiology. Evolutionary perspectives upon human reproductive behaviour are often complex (and controversial) issues; in this context, specific and more isolated mechanisms for possible evolutionary stability of some LLBC—e.g., related to male-female behavioural interactions—are suggested for further discussion. Overall, it is proposed that qualitative and quantitative changes in hormones and hormonal activities during the menstrual cycle evolved to optimize reproductive success through neurophysiological responses that include behavioural components, perhaps some LLBC.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".