The Case for a Constitutional Right to Barrier-Free Voting for Electors with Disabilities
Bibliographic record
Abstract
Through a constitutional challenge at the Federal Court of Canada, formal limits on the right to vote for peoples with disabilities were abolished in 1988. Despite this expansion of formal voting rights, peoples with disabilities continue to face barriers in Canadian electoral processes with lower rates of voting and continued reports of barriers in casting a ballot. In this article, I explore voting barriers for electors with disabilities from a constitutional perspective. Instead of focusing on potential legislative and administrative responses to voting barriers, I seek to make the broader case for the constitutional right to a barrier-free voting for electors with disabilities. Specifically, I argue that the right to vote under section 3 of the Canadian Charter of Rights and Freedoms imposes a positive obligation on federal, provincial, and territorial governments to offer barrier-free voting options for electors with disabilities. The recognition of a section 3 right to vote without disability barriers creates a minimum constitutional threshold for any future legislative or executive action (or lack thereof) that impacts electors with disabilities. My proposed right to barrier-free voting for electors with disabilities has three components: (1) the right to a private vote, (2) the right to actively cast a vote, and (3) the right to verify a vote.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".