Engaging Citizens with Disabilities in eDemocracy
Bibliographic record
Abstract
We evaluate access for people with disabilities in two Canadian federal government eConsultations — the development of its Innovation Strategy and as part of the Parliamentary sub-Committee on People with Disabilities consultations around the Canada Pension Plan — Disability hearings. From qualitative interviews with government and the disability community as well analysis of key documents, we illustrate what worked in ensuring access for Canadians with disabilities and what served to create additional barriers to access. We suggest, first, that accessibility is not the same thing as usability and requires meeting, at minimum, commonly held standards of access. Secondly, we argue that access is not enough to bring people with disabilities into eConsultations. Proactive measures to reach people experiencing a wide spectrum of disabilities are essential to "enfranchising" people with disabilities in eDemocracy. Addressing both access and inclusion are simply good public policy, not extraordinary measures to address a minority population.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.002 |
| 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".