Delinquency Prevention through Crime Conservation
Bibliographic record
Abstract
Nowadays, the prevention of crime over the past are subjected to the judicial authorities seriously and criminal policy has adopted a set of measures to deal with criminal phenomenon that a part of the measures returns to situational prevention measures which seeks to limit the opportunities and situations causing offense and makes difficult to realize the criminal mind. Further, one of these strategies, protect and support the target of crime. In addition the community has not been achieved in take away of the mind and think of the people from crime and social prevention, perfectly. Therefore, we must think of the stockade after the realizing of the criminal act. Owing to one of the ways in which the transition from thought to action make difficult is strengthening the protection of crime targets so the aim of the choice of the current title is trying to realize to prevention from delinquency by protecting the target of crime. Moreover, research methodology is explanatory method using the library resources, the finding of the author of this study is that the organized protection of targets that are more vulnerable to crime will be an effective step towards restriction the crime. In conclusion this protection will be including outside the in-hand targets of criminals or exposed them in the public view, technical measures of protection of the homes and vehicles and other property, property marking, control of inputs and outputs, electronic protections such as video surveillance and protection from software data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".