Revisiting Deterrence: Legal Knowledge, Use Context and Arrest Perception for Cannabis
Bibliographic record
Abstract
Deterrence research has evolved considerably since the 1970s when a favourite offence for study was the emerging phenomenon of widespread cannabis use among mainstream populations. The deterrent model of crime prevention has expanded far beyond the study of objective and subjective indicators of certainty and severity to encompass social support, moral evaluations, peer involvement and, most recently, risk sensitivity and situational factors. Most earlier research found no evidence of deterrence of cannabis use, a fi nding attributed to its low actual and perceived risk of arrest and changes in public attitudes. This interview study with adult experienced cannabis users drawn from a representative survey base in Toronto, Canada, where possession is still treated as a criminal offence, provides a more nuanced interpretation of deterrence. The authors found that users had generally inaccurate knowledge about the current law and penalties and believed they would avoid arrest in the future. However, they were not oblivious to the possibility of police intervention, and took precautions such as carrying small amounts and avoiding public use. Thus, users were not unaffected by the law, but rather these discreet practices illustrate the operation of restrictive deterrence, shaping their choices of when, where and how to commit the crime of cannabis use. Further research on deterrence should examine situated choices by risk-sensitive offenders, and should also include cross-national patterns of arrest and user risk perceptions. While cannabis continues to be prohibited by international treaties, the reality of widespread use presents opportunities for innovative deterrence studies into this normalised yet illegal crime. The variation in policies, penalty structures and enforcement across European nations, compared to those in other Western jurisdictions, can foster relevant research for a transatlantic discussion about global drug policy transformation.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".