Drugs, alcohol, and criminal behaviour : a profile of inmates in canadian federal institutions
Bibliographic record
Abstract
The scientific literature often mentions that there is a statistical connection between alcohol and drug consumption and criminal behaviour. However, there is little information available which would make it possible to quantify this connection, and specify the impact that drugs and alcohol have on criminal behaviour. Consumption of psychoactive substances has two major effects: intoxication and addiction. These effects are related, respectively, to the psycho-pharmacological and economic-compulsive models of the connection between drugs and crime. The first model associates drug use and intoxication with a decrease in cognitive functions and a lack of self-control, leading to aggressive impulses, violence and lack of inhibitions. The second model refers to the huge costs that are associated with being addicted to certain drugs. A person addicted to these drugs would need to engage in lucrative criminal activities in order to pay for them. This article explores and attempts to further define the links between alcohol, illicit drugs and criminal behaviour, taking into account the types of drugs consumed and the types of criminal behaviour displayed.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".