Jealousy Mediates the Link Between Women’s Upward Physical Appearance Comparison and Mate Retention Behavior
Bibliographic record
Abstract
Previous research has demonstrated that men's lower mate value predicts increased perpetration of mate retention, especially with respect to cost inflicting behaviors. It is less clear if lower mate value women, including those who perceive themselves as being less physically attractive than their intrasexual rivals, also perpetrate more mate retention. Moreover, it is presently unclear whether romantic jealousy, which has been proposed to motivate compensatory behavior in response to evidence that a valued mating relationship is threatened, might mediate this link. The present study addressed this gap in knowledge by examining whether women's overall self-perceived mate value and upward physical appearance comparisons predicted their cost inflicting and benefit provisioning mate retention, as well as whether jealousy mediated these relationships. In a sample of 167 heterosexual undergraduate women, results found self-perceived mate value predicted greater benefit provisioning mate retention, but not romantic jealousy. In contrast, jealousy mediated the relationship between women's upward physical appearance comparisons and both their cost-inflicting and benefit-provisioning mate retention, supporting the hypothesis that jealousy in the face of unfavorable social comparisons on important mate value traits can promote action aimed at retaining a mate.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".