Bibliographic record
Abstract
Abstract Life-history theory provides a framework for understanding the resource trade-offs that are inherent in the struggle to maximize reproductive fitness. Hormones, and testosterone in particular, play important roles in mediating some of the morphological, behavioral, and physiological traits that are implicated in these trade-offs—one of the most widely studied of which is mating versus parenting. In this chapter, we use a life history perspective to review literature examining hormones and infidelity or related proxies (e.g., interest in extrapair sex), and how these links may be understood as a function of the mating vs. parenting trade-off. This chapter focuses the review primarily on testosterone, but also reviews other hormones and hormone systems that have been implicated in infidelity or relevant behavioral and psychological proxies. Further, it touches on contextual considerations for understanding the link between hormones and infidelity and mating, such as the type of mating system (e.g., monogamy vs. polygamy) and the menstrual cycle. The chapter concludes with a discussion of some of the limitations of current, and potential avenues for future, research on hormones and infidelity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".