The Role of State Attorney General in Prevention of Crime Occurrence
Bibliographic record
Abstract
Nowadays, wide span and diversity of new types of crime have provoked a social crisis. To prevent the crime, various reactions have been revealed in different societies. However, such responses are originated in retributive and intimidation thoughts. Public opinion and lack of knowledge in properly treating with crimes have been the main reasons why governments tend to repressive reactions, while prevention of crime occurrence topics should be considered in the first priority of crime policies of countries. Attorney general’s appearance in prevention realm is complicated and sensitive and deemed as a most challenging subject. Because he in charge of public prosecutor is the keeper of individuals, social, government, people’s benefits and guardian of social security. But his range of intervention is under question and is a serious and challenging argument. The present study aims to know the different kinds of challenges and available solutions in the light of explaining the dissuasive methods and also to prevent the increasing of crimes number and social security threat by the most effective tools and prevention methods and perform our duties favorably and properly in accordance with meet the needs of criminal justice goals. The method used in this study is analytic-descriptive and is prepared using library valid documents and books. We conclude that in social prevention level, the governmental organizations are not the only effective and responsible but by considering international experiences in performing patterns of prevention management, it seems that performing prevention plans through social institutions and NGOs (particularly in social prevention)is highly effective in crime occurrence prevention.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".