Non‐linear associations between stature and mate choice characteristics for American men and their spouses
Bibliographic record
Abstract
OBJECTIVES: Although male height is positively associated with many aspects of mate quality, average height men attain higher reproductive success in US populations. We hypothesize that this is because the advantages associated with taller stature accrue mainly from not being short, rather than from being taller than average. Lower fertility by short men may be a consequence of their and their partner's lower scores on aspects of mate quality. Taller men, although they score higher on mate quality compared to average height men, may have lower fertility because they are more likely to be paired with taller women, who are potentially less fertile. METHODS: We analyzed data from The Integrated Health Interview Series (IHIS) of the United States (N = 165,606). Segmented regression was used to examine patterns across the height continuum. RESULTS: On all aspects of own and partner quality, shorter men scored lower than both average height and taller men. Height more strongly predicted these aspects when moving from short to average height, than when moving from average to taller heights. Women of a given height who scored lower on mate quality also had shorter partners. CONCLUSIONS: Shorter men faced a double disadvantage with respect to both their own mate quality and that of their spouses. Scores of taller men were only marginally higher than those of average height men, suggesting that being tall is less important than not being short. Although effect sizes were small, our results may partly explain why shorter and taller men have lower fertility than those of average stature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".