Watch Your Language: A Review of the Use of Stigmatizing Language by Canadian Judges
Bibliographic record
Abstract
Despite ongoing advances in understanding the causes and prevalence of mental health issues, stigmatizing language is still often directed at people who have mental illness. Such language is regularly used by parties, such as the media, who have great influence on public opinion and attitudes. Since the decisions from Canadian courtrooms can also have a strong impact on societal views, we asked whether judges use stigmatizing language in their decisions. To answer this question, we conducted a qualitative study by searching through modern Canadian case law using search terms that were indicative of stigmatizing language. We found that, although judges generally use respectful language, there are still many instances where judges unnecessarily choose words and terms that are stigmatizing towards people with mental illness. We conclude that, to help reduce the stigma associated with mental illness, judges should be more careful with their language. co-author: Michelle Black
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".