NDPI-predict: A Multi-dimensional Model for Simulating Violent Behavior Risk
Bibliographic record
Abstract
Violent behavior poses a significant threat to both societal and individual security and has been consistently associated with Callous-Unemotional (CU) traits. These traits, defined by diminished empathy and guilt, are linked to structural and functional brain alterations, including reduced gray matter in the paralimbic cortex, orbitofrontal cortex, and anterior cingulate cortex, alongside decreased connectivity within empathy-related neural networks. Moreover, exposure to childhood trauma and heightened reactivity to violence may intensify CU-related dispositions, thereby elevating the risk of aggression and premeditated harm.Drawing upon the Neurodevelopment Pathway-Driven Intervention (NPDI) model, this study explores the neural and psychological mechanisms underlying CU traits and their contribution to violent behavior, with specific attention to sex-related variations. To evaluate risk, a machine learning framework was developed incorporating key performance metrics (Accuracy = 0.92; AUC = 0.95) and integrating multimodal data sources, neural biomarkers (gray matter volume, functional connectivity, amygdala reactivity), personality indices, and sex. Results distinctly differentiate high-risk from low-risk groups, demonstrating the model’s robust predictive capability. These findings underscore the interplay between neurobiological and personality dimensions of CU traits and highlight the model’s potential application in forensic risk assessment and early intervention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".