An Explanation of Human Rape: An Integration of Sociobiology and Social Science
Bibliographic record
Abstract
The study of human rape within a sociobiological framework has been a topic of public debate for decades, most notably after the release of biologist Edward O. Wilson’s book Sociobiology: The New Synthesis (1975). As sociobiology is based on the theory of evolution, Wilson’s book analyzed the social behaviour of animals, thus asserting that their social adaptations can be compared to the social behaviours of human-beings (Clark, 1991). Through this sociobiological framework various social behaviours of humans were addressed for further study, including the controversial subject of human rape (Clark, 1991). Among the supporters of this framework, none are as infamous as biologist Randy Thornhill and anthropologist Craig Palmer, who co-wrote A Natural History of Rape: Biological Bases of Sexual Coercion (2000). This notorious book outlines several possible human rape adaptations, virtually all of which concern reproductive strategies. The works of such authors have been criticized by the social sciences, including feminist academics like Susan Brownmiller, who claim that rape is not about sex, but power and domination (Thompson, 2009). Meanwhile, many sociobiology supporters, including Thornhill and Palmer (2000), maintain that rape is about sexual desire, and claim that the social sciences lack merit in their research on rape because their theories do not consider the evolutionary causes of human behaviour (p. xi). As this essay will demonstrate, both cultural and evolutionary forces have been shown to have considerable effects on the occurrence of rape. Therefore, I argue for an integration of both approaches in order to successfully understand and thus potentially prevent and eradicate rape.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.035 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".