Access to Justice Online: Are Canadian Court Websites Accessible to Users with Visual Impairments?
Bibliographic record
Abstract
Steps taken to make legal information available online have resulted in access to justice benefits for many. However, these benefits may not extend to everyone equally. As scholars have cautioned, the adoption of new technologies that purport to improve access to justice may perpetuate the exclusion of vulnerable and marginalized individuals and groups from the justice system. This article applies this insight to legal information made available online by Canadian court websites and CanLII. It does so through a two-part study. First, we used an automated testing tool to determine whether the websites noted above comply with accessibility standards. Second, after having secured research ethics approval, we worked with Access & Diversity at the University of British Columbia to recruit persons with visual impairments; these participants evaluated the same websites and provided feedback. Our results showed that while largely accessible, the tested websites fall short of best practices, presenting challenges to users with visual impairments. We recommend that Canadian courts correct the deficiencies identified by our study, that other online legal resources be tested for accessibility issues, and that future research focus on the extent to which online legal resources are accessible to other vulnerable or marginalized individuals or groups. Implementing these recommendations will ensure that the access to justice benefits of online legal information are extended to everyone.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".