People With Disabilities Need Lawyers Too! A Ready-To-Use Plan for Law Schools to Educate Law Students to Effectively Serve the Legal Needs of Clients With Disabilities as Well as Clients Without Disabilities
Bibliographic record
Abstract
Canada's legal profession is not sufficiently equipped to meet the legal needs of clients with disabilities. For decades, legal education focused primarily, if not exclusively, on training law students to serve clients without disabilities. A law student can complete their legal education while learning little about how to meet the legal needs of clients with disabilities. Law students need to be effectively trained to serve clients with disabilities as well as clients with no disabilities. Law faculties commendably focus increasingly on Equity, Diversity, and Inclusion. Disability should be a strong and equal focus in their equity, diversity and inclusion strategies. How can a law school fix this? This article gives a roadmap, and gives further resources enabling law deans and law teachers to quickly take action. This article first describes why it is important to expand a law school's disability curriculum. It spells out disability content that should be shared with students, including a course-by-course delineation of topics. It offers practical, cost-effective options for law schools to systematically work towards permanently embedding disability content in their programs. A law school should make a concerted policy decision and create an action plan. This article’s tools point the way.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".