The Influencing Factors of Psychological Causes of Violent Crime
Bibliographic record
Abstract
Violent crimes not only can cause significant economic losses, but also affect people's mental health. This passage discusses the various psychology factors that form violent crimes in combination with literature. The group of violent criminals usually has cognitive defects, which are manifested in poor judgment, serious distortion of values and morals, and are easily influenced by wrong ideas. In addition, this type of criminals are also generally emotionally unstable, vulnerable to emotional frustration, and often dominated by strong negative emotions such as anger and resentment. Thirdly, in terms of personal quality, the violent criminal group also has defects, that is, the criminals have poor self-regulation and control ability, and are prone to lose control or over-suppress emotions. Finally, from the analysis of personality and personality psychological factors, it is pointed out that violent criminals usually have obvious negative personality traits, such as impulsiveness, lack of responsibility and sympathy, and excessive expansion of self-awareness. These personality defects may be the root cause of their violent means. In general, these different psychological factors interact with each other and jointly influence and form the psychological mechanism of violent crime. This article can provide some support for understanding the causes and prevention of violent crimes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".