Short-term and long-term mate preference in men and women in an Iranian population
Bibliographic record
Abstract
Mate preference in short-term relationships and long-term ones may depend on many physical, psychological, and socio-cultural factors. In this study, 178 students (81 females) in sports and 153 engineering students (64 females) answered the systemizing quotient (SQ) and empathizing quotient (EQ) questionnaires and had their digit ratio measured. They rated their preferred mate on 12 black-line drawing body figures varying in body mass index (BMI) and waist to hip ratio (WHR) for short-term and long-term relationships. Men relative to women preferred lower WHR and BMI for mate selection for both short-term and long-term relationships. BMI and WHR preference in men is independent of each other, but has a negative correlation in women. For men, digit ratio was inversely associated with BMI (p = 0.039, B = - 0.154) preference in a short-term relationship, and EQ was inversely associated with WHR preference in a long-term relationship (p = 0.045, B = - 0.164). Furthermore, men and women in sports, compared to engineering students, preferred higher (p = 0.009, B = 0.201) and lower BMI (p = 0.034, B = - 0.182) for short-term relationships, respectively. Women were more consistent in their preferences for short-term and long-term relationships relative to men. Both biological factors and social/experiential factors contribute to mate preferences in men while in women, mostly social/experiential factors contribute to them.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".