Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Throughout history and in all cultures, men are measurably more prone than are women to engage in killing other persons, whether in self-defense (justifiable homicide), misperceived threat, or murder (unlawful homicide). There are compelling reasons for this disparity of aggression, related to our evolution as a species. This article focuses on the nature aspect of the nature–nurture interaction, which has greater explanatory power for the male “excess” of aggression and killing. Neuroendocrine (especially, testosterone-related) and neurophysiological factors (those relating to the emotions of rage and lust) underlay this sexual dimorphism. Viewed in this light, killing—particularly murder—by men may be understood as exaggerations of otherwise adaptive tendencies directed at the preservation of the group, and at the retention of one's social status and of one's mate. Particular attention is paid to several types of killing by men. Uxoricide (the killing of one's wife) may be prompted by such motives as jealousy (directed at the threat of losing one's wife) or lust (killing one's wife so as to be free to mate with another woman) or greed (related to the desire to retain or improve one's social status by gaining insurance money or inheritance). The threat of social exposure and humiliation (by a wife who knows embarrassing secrets) is yet another motive. Additional topics are also addressed, such as serial sexual homicide—an exclusively male phenomenon—and stalking, which is often aimed at coercing an otherwise unwilling woman to become one's sexual partner. The final section addresses the increase in recent years for certain men—of a particularly callous nature—to commit murders of striking depravity, even at a time when the murder rate in general is decreasing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 it