More Than Voting Booths: Accessibility of Electoral Campaigns for People with Disabilities in Ontario
Bibliographic record
Abstract
Obstacles to electoral involvement for persons with a disability are not limited to inaccessible polling sites. Meeting venues, campaign offices and constituency offices are all central to the effective functioning of Canadian democracy. The purpose of this paper is to identify the extent to which the Ontario election campaign of 2011 “opened doors” to electoral participation for persons with disabilities. The study used a survey and document review approach to compose a snapshot of election and campaign accessibility in Ontario in 2011. Party leaders were polled to seek their official position on disability issues and accessibility in their campaign and their platform. Thirty individual candidates were approached from each of the 3 official parties and from 10 ridings across Ontario. Referring to the 2011 Ontario provincial election, candidates were asked about campaign offices, candidate meetings and website accessibility. Websites and campaign materials were also reviewed for the three parties for any mention of disability or accessibility. The findings from this survey suggest that there is a general lack of understanding of the imperative to achieve accessibility standards, not only of polling stations and booths, but also of political campaigns, if representative democracy in Canada is to include people with disabilities.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".