Women's Romantic Jealousy Predicts Risky Appearance Enhancement Effort
Bibliographic record
Abstract
Human appearance enhancement effort has recently been considered from an evolutionary perspective as an adaptive and sexually dimorphic strategy for effective female intrasexual and intersexual competition. Most writing and research on the topic to date has focused on appearance enhancement as a means of mate attraction, with relatively less research examining its role in mate retention. The present study considered whether romantic jealousy, as a negative emotion experienced in response to perceived threat to a desired relationship, predicts costly and/or risky appearance enhancement independent of the closely related emotion of envy. In a sample of 189 undergraduate women, results showed that romantic jealousy and dispositional envy were positively correlated with one another. Results further demonstrated that romantic jealousy predicted women's positive attitude toward cosmetic surgery, willingness to use a one-week free tanning membership, willingness to use a risky diet pill, and intent on spending a greater proportion of their income on appearance enhancement, but not intended use of facial cosmetics. Results held independent of participants' dispositional envy, suggesting that romantic jealousy is a unique predictor of women's efforts at enhancing their physical appearance, which could extend into costly and physically risky mate retention efforts.
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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 | 0.004 |
| 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 | 0.000 |
| 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".