The Relationship between Addiction and Socio-demographic Characteristics of Iranian Newcomer Prisoners
Bibliographic record
Abstract
Prison has proven to be a suitable environment for the identification of various socio-demographic characteristics of those individuals whose drug use, or related crimes lead to incarceration. Furthermore, the prison environment could also support increased understanding of both the pattern and the relationship between drug use and the incidence of crime and deviances. Using the survey method, this study examines the socio-demographic features of 2200 prisoners in seven provinces of Iran. More specifically; drug abuse patterns and the relationship among addiction, crime prevalence, and some personal as well as socio-demographic characteristics were studied. According to the findings, characteristics such as age, education level, economic status, urban and/or rural status, all have an effects on the rate of drug use and, on crime commitment and its re-occurrence. Accordingly, younger age, lower socioeconomic status and urban residence showed a relationship with tendency to commit crime and repeat it while employment had no significant effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".