Bibliographic record
Abstract
Evolutionary explanations come in two basic forms. Functional explanations focus on elucidating the adaptive (i.e., reproductive) value of a particular behavior. Phylogenetic explanations focus on understanding the evolutionary history of a behavior: that is, how it originated and changed in a step-by-step manner over time. Cross-species comparisons indicate that same-sex sexuality is not an evolutionarily uniform phenomenon. Multiple analogous forms of homosexual behavior have evolved. I argue that our understanding of why same-sex sexualities evolved is contingent on the implementation of both types of evolutionary analysis. I describe research on female bonobos (Pan paniscus) to illustrate how functional investigations can help explain why some forms of primate homosexual behavior evolved, such as those that are sociosexual in nature. I then describe my research on female Japanese macaques (Macaca fuscata) to illustrate how phylogenetic investigations can help account for how other forms of homosexual behavior evolved, such as those that are strictly sexual in nature. I conclude by discussing how both functional and phylogenetic perspectives need to be fully integrated to account for these data and, by extension, the evolution of male homosexuality in humans.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".