Bibliographic record
Abstract
This chapter examines the experiences of Canadian signed language interpreters and members of the deaf community in navigating the legal system. As a marginalized linguistic minority, deaf individuals face persistent structural barriers across political, educational, healthcare, and vocational contexts, which become even more severe when they engage with the justice system. Drawing on findings from a national study of deaf, hard of hearing, and deafblind Canadians, the chapter traces encounters from initial contact with police or social workers to court proceedings and correctional settings. The study reveals ongoing inequities in access to interpretation, communication, and culturally competent support for both victims and incarcerated persons. While recent initiatives—such as provincial interpreter training and collaborations with deaf mental health professionals—mark incremental progress, systemic problems remain. A 2024 human rights tribunal, for instance, found that deaf prisoners continued to face violations of linguistic rights, with inadequate interpreting provision and outdated communication technologies. These findings highlight that while interpreting services are essential, they alone cannot ensure full linguistic justice. Broader structural reforms are required to affirm deaf individuals’ rights and guarantee equitable participation in all stages of the legal process. The chapter concludes with recommendations for policy and institutional change.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.049 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 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".