Male androphilia, fraternal birth order, and female fecundity in Samoa: A 10-y retrospective
Bibliographic record
Abstract
Two separate but related literatures have examined familial correlates of male androphilia (i.e., sexual attraction and arousal to masculine adult males). The fraternal birth order effect (FBOE) is a widely established finding that each biological older brother a male has increased the probability of androphilia 20-35% above baseline rates. Other family demographic variables, such as reproduction by mothers, maternal aunts, and grandmothers, have been used to test evolutionary hypotheses that sexually antagonistic genes lead to androphilia among males, lowering or eliminating reproduction, which is offset by greater reproductive output among their female relatives. These proposed female fecundity effects (FFEs), and the FBOE, have historically been treated as separate yet complementary ways to understand the development and evolution of male androphilia. However, this approach ignores a vital confound within the data. The high overall reproductive output indicative of an FFE results in similar statistical patterns as the FBOE, wherein women with high reproductive output subsequently produce later-born androphilic sons. Thus, examination of the FBOE requires analytic approaches capable of controlling for the FFE, and vice-versa. Here, we present data simultaneously examining the FBOE and FFE for male androphilia in a large dataset collected in Samoa across 10 y of fieldwork, which only shows evidence of the FBOE.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".