Violent and Agressive Behaviour: Are we Defining the Problem Appropriately?
Bibliographic record
Abstract
Violent behaviour has an abundance of negative consequences ranging from the victim’s personal distress to increased economic strain on an entire community. The search for the cause(s) of aggression has been long underway with some major findings and innovations. However, there have also been several overlooked topics pertaining to the increased likelihood of violence. The current review aims to examine one of these oversights: the deeper meaning of trait aggression and the role of emotional self-regulation in violence perpetration. The author reviews current literature on the topic and outlines several correlations between violent behaviour and emotional self-regulation, which appear to be consistent across multiple adult populations. The author argues that this strong link provides due cause for future research aimed at empirically examining the relationship between emotional self-regulation and violent behaviour.
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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 | 0.028 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.015 | 0.042 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.019 | 0.029 |
| 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".