Women's Life History Attributes are Associated with Preferences in Mating Relationships
Bibliographic record
Abstract
Life history theory (LHT) is a powerful framework for examining relationship choices and other behavioral strategies which integrates evolutionary, ecological, and socio-developmental perspectives. We examine the relationship between psychological and behavioral indicators of women's life history attributes and hypothetical relationship choices with characters representing short-term and long-term male sexual strategies. We demonstrate that psychological indicators of women's life history strategies are related to predicted and actual behaviors in mating relationships. Women with insecure attachment styles, especially those with negative evaluations of both themselves and others (fearful attachment), were more likely to consider men with attributes indicating short-term mating strategies for short-term and long-term relationships than women with a secure attachment style. Women with relatively unrestricted sociosexuality were more likely to predict they would have sexual affairs with men in general, with the tendency being generally stronger when considering men with attributes indicating short-term mating strategies. Those who scored high on self-monitoring were also more likely to predict having sexual affairs and short-term relationships with these men. These and other findings demonstrate the usefulness of a life history approach for understanding women's relationship choices.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".