Men display faster in male-biased mating contexts
Bibliographic record
Abstract
Across diverse species with sexual reproduction, typically the more male-biased the adult sex ratio (ASR), the greater the investments by the more populous males in the rarer females who hold greater bargaining power in a mating context. Relatively few studies have examined this effect in humans however, and almost none involve observations of actual male investment in a potential mating context. Here, we present one of the first studies to observe investments of men in a potential mating context under differing ASRs. Across 163 mixed-sex groups from three taverns on 7 days of observation, we measured both a group's ASR and each group's leading man's latency to position himself at the tavern's bar to order and pay for beverages. The higher the proportion of men in a group (ASR) and the fewer the absolute number of women in a group, the faster the leading man in the group travelled to reach the bar to order and pay for beverages. Results are consistent with the hypothesis that similar to males in many species, men tactically regulate their investments to adapt to the fluctuation in the ASR in order to maximize their probabilities of attracting 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 | 0.000 | 0.001 |
| 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) | 0.006 | 0.001 |
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".