Incorporating sociocultural and situational factors into explanations of interpersonal violent crime
Bibliographic record
Abstract
This review adapts a previously prescribed multifactorial model of multiple perpetrator sexual offending (Harkins, L., & Dixon, L. (2010). Sexual offending in groups: An evaluation. Aggression and Violent Behavior, 15(2), 87–99.) to more fully inform explanations of different types of interpersonal violent crime. First, factors within the sociocultural and situational contexts of the model are reviewed, as well as the interactions between them and the individual context, to examine their role in explaining a broad range of violent crimes. Exemplars of street-gang and intimate partner violence are then examined to assess how the empirical evidence supports the proposed framework. It is concluded that the adapted multifactorial model lays the foundations for fuller causal explanations of violent crime without restricting the focus to a specific crime type, or level of explanation, in addition to bridging interdisciplinary theoretical gaps.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".