Relationship between alcohol and occurrence of various offenses
Bibliographic record
Abstract
Alcohol is the most prevalent abused drug in the world and brings out a large amount of medical expenses to the society.This paper presents an empirical investigation to devise preventive measures and identify the groups at risk.In this study, a descriptive study was conducted among people who were arrested by the police because of committing illegal actions and were suspected of alcohol usage were referred to the organization of forensic medicine.The studied variables include gender, age, state of affair, marital status, type of crime, the time of drug use, tranquillizer drugs and the times of psychiatrist visit.Out of 305 subjects, 204 (66.9%) cases had a positive respiratory test result and 101 (33.1%) were negative.The results showed that committing crimes due to quarrel and wrangling (34.3%) and being drunk in the public (29.7%)were prevalent among the people who used alcohol.Driving accidents also had 18% prevalence.32.7% of the people who had a negative respiratory test result had committed crimes because of being drunk and 29.7% of them had quarrel and wrangling.The high rate of alcohol use in people who referred from the disciplinary office to the organization of forensic medicine demonstrated that it was necessary to devise a plan in order to improve a person's awareness of harmful alcohol use and to identify the vulnerable groups in the society.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".