Bibliographic record
Abstract
Abstract There are numerous complementary approaches to the biology of aggression, ranging from genetic to cognitive research. Arguably, the most successful of them have been guided by hypotheses derived from evolutionary theory. In contrast to the view that human aggression is symptomatic of psychological impairment, social disorganization, or both, evolution-minded hypotheses typically begin from the premise that aggression has been designed by natural selection to serve one or more adaptive functions, and that the mechanisms involved can be sensitive to cues of reproductive consequences in the social environment. Specifically, anatomical, physiological, and psychological adaptations for aggression are expected to evolve when they help individuals secure resources and matings for themselves and for their genealogical kin. From a theoretical perspective, contexts of predation, sexual competition, and sexual conflict are especially likely to foment aggression. A considerable body of research on aggression in nonhuman animals reinforces the adaptationist position, and central findings of this viewpoint—such as differential risk of violence according to sex and kinship—are closely mirrored in humans. Although many features of human aggression are likely the result of adaptations designed to yield these very features, others are more plausibly understood as byproducts of adaptations designed for different purposes. In either case, evolutionary approaches can help to identify the mechanisms underlying aggression and thereby provide ways to reduce its impact.
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 | 0.001 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".