Individual's Reproductive Strategies Moderates the Association Between Facial Width-to-Height and Risk-Taking Propensity
Bibliographic record
Abstract
Previous research has yielded mixed findings on the relationship between facial width-to-height ratio (fWHR), an androgen-dependent feature, and risk-taking propensity. We argue that mixed findings might result from overlooked variables. Given that risk-taking propensity might be ultimately linked to a search for mating opportunities, we analyze if reproductive strategies moderate the relationship between fWHR and risk-taking propensity. Our results, obtained from a sample of 434 male participants, show a positive association between fWHR and recreational and social risk-taking only for men who are more motivated to focus on mating effort over offspring survival. This finding aligns with research arguing that risk-taking may be a mating strategy since being social and recreational risk-prone might illustrate physical and psychological qualities and improve one's ability to attract mates. Our results support the notion that risk-taking might be a domain-specific construct. Overall, our research is in line with recent findings suggesting that the impact of testosterone exposure on risk-taking propensity is best understood when considering the role of contextual variables. Consequently, we add to previous research that studies related to risk-taking propensity should account for reproductive strategies.
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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 | 0.003 |
| 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) | 0.005 | 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".