Watch Your Language: A Review of the Use of Stigmatizing Language by Canadian Judges
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it