A Bad Deal: British Columbia’s Emphasis on Deterrence and Increasing Prison Sentences for Street-Level Fentanyl Traffickers
Bibliographic record
Abstract
An analysis of the British Columbia fentanyl sentencing decisions reveals that courts are emphasizing the need for enhanced deterrence as a response to the opioid crisis.Increasing prison sentences is not an evidenced-based response to this public health crisis.In the street-level trafficking cases examined, 12 of the 14 people were motivated to traffic to support their own addiction.The courts' response of lengthening custodial sentences for people who are trafficking fentanyl will not deter street-level trafficking.Instead, the court's punitive approach will increase the number of people in custody, and disproportionately impact Indigenous people and those with substance abuse issues.Lengthier prison sentences should not be the prescribed response by the courts to deal with this public health crisis.The courts' response to the opioid crisis exacerbates the present risks to people who use drugs and puts a vulnerable population at an increased risk of harm.* This article comprises chapter two of my LL.M thesis at the University of British Columbia entitled "The Opioid Crisis as Health Crisis, Not Criminal Crisis: Implications for the Criminal Justice System".While I am thankful to many people for their assistance and support, I would like to specifically acknowledge the insightful
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".