Discursively Embedded Institutionalized Stigma in Canadian Judicial Decisions
Bibliographic record
Abstract
The aim of this research was to explore how the concept of harm is constituted in case law judicial decisions pertaining to the importation, production, possession, and trafficking of drugs in Canada using critical discourse analysis methodology. The research was designed to uncover taken-for-granted assumptions about drugs and associated harms. The data source for this study is judicial decisions. These are published texts where judge(s) summarize details about the factors considered, provide a reasoned interpretation of sentencing principles relevant to the judicial decision, and explain the rationale for their decision. Initially, codes were identified deductively, using words related to drugs and harm. Codes were added when incidents of moralization language were observed to be high. Moralization language was defined as “the usage of language cues referencing moral values”. The selection process resulted in n = 129 judicial decisions meeting the inclusion criteria. Discourse analysis was guided by four tools described by Gee’s study: the significance tool, the why this way and not that way tool, the connections tool, and the intertextuality tool. Emergent themes are: (1) trafficking as an immoral enterprise; (2) scourge to society, (3) fentanyl and harm, and (4) constructing gravity. This study uncovers discursive practices in many judicial decisions that convey the (re)production of institutionalized stigma. High reliance on legal tropes about drug harms, harm of trafficking, moral culpability associated with distribution of some drugs, by some people, in some ways, and a lack of contextual awareness of social inequities that influence the lives of Canadians perpetuates legal interpretations that support rationales for sentence predicated on denunciation and deterrence.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.041 | 0.050 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".