Criminal Responsibility of Offenders with Personality Disorders with an Emphasis on Crime Psychological Factors
Bibliographic record
Abstract
A variety of psychiatric disorders but dementia are among the issues discussed in criminology and can have a significant influence on the criminal responsibility of the perpetrators and the patients with such disorders and consequently on their criminal law. Mental disorders which encompass a wide range of mild, moderate, and severe neuropsychiatric illnesses are usually resulting from biological-psychological-social factors which make up a person's personal and social environment. Susceptibility to these disorders can be studied from different perspectives, including the law and criminal law perspective. Legislator with the knowledge and understanding of this issue has always made an attempt to lay down rules which fir this unfortunate phenomenon and has also taken measures in this regard. However, the achievements of medical sciences, especially psychology and psychiatry, suggest that some mental disorders, due to the expropriation of belonging, understanding, and determination, nullify the criminal responsibility and a number of disorders, due to having influence and pressure on reasoning, understanding, and decision-making, reduce the criminal liability. Due to lack of having influence on individuals' reasoning, understanding, and decision-making, some others do not have effect on criminal responsibility; however, they put mental health at risk.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".