Bibliographic record
Abstract
Purpose This paper seeks to review transitional processes that foster transitions from non‐aggression to extreme aggression. Most studies on aggression focus either on traits within violent individuals or social contexts that generate violence, less attention has been paid to transitional mechanisms. Design/methodology/approach The paper reviews “long‐term”, i.e. societal transitions that occur prior to and during genocides; mid‐term transitions such as induction into a military or paramilitary societies; and short‐term (situational) transitions that occurin situ. It reviews alterations in emotion, cognition, and behaviour that occur in these transitions and concludes with a description of the generated output behaviours of an extreme and often sadistic nature. Findings The paper concludes with a review of Nell's “Pain‐Blood‐Death” complex as a hypothetical inherited disposition that may be triggered by any or all of these transitional processes leading to cruel aggression. Originality/value The paper raises new concerns about the conceptualizing of extreme violence purely as an outcome of individual pathology and posits instead that a potential for cruelty may be part of our sociobiological heritage as a species. Furthermore, this potential may be tapped into by exposure to toxic war situations resulting in the manifestation of cruel and inhumane treatment of outgroups by soldiers from disparate societies and eras.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".