Representations of Drug-Related Harms in Sentencing: Towards Evidence-Informed, Non-Stigmatizing Approaches
Bibliographic record
Abstract
This thesis explores how the concept of harm is constituted in case law pertaining to the importation, production, possession, and trafficking of drugs in Canada. A specific focus of analysis is whether judges accurately use empirical research to inform decisions. Drugs are understood as a social construct – encompassing psychoactive substances that are both legal and illegal – and as variably regulated in Canadian law. I use critical discourse analysis to examine how harm is represented in case law (n=129), identify which sources influence legal discourses (e.g., past cases, expert testimony, empirical research), and analyze outcomes arising from how harm is constructed in case law. This approach indicates that normative use of moralization language silences certain knowledge sources and contributes to institutionalized stigma. Recommendations for reform include incorporating critical reflectivity into judicial practices, accurately representing harm in ways that are non-stigmatizing, and improving research literacy skills.
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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 | 0.135 | 0.184 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.005 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| 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".