Narrative Policy Analysis of the Accessibility for Ontarians With Disabilities Act in the municipal recreation context
Bibliographic record
Abstract
Since its introduction in the legislature in 2005, the Accessibility for Ontarians with Disabilities Act (AODA) has largely been ignored by the research community. 2008 marked the implementation of the first of five accessibility standards, the Customer Service standards, in accordance with the AODA. These standards state that goods and services must be made accessible to people with disabilities (Statistics Canada, 2006). While this legislation is still in its early phase, it is crucial to conduct research at this point in the process to better understand the ways in which the legislation is expressed in practical terms. Furthermore, while the AODA is critical for people with disabilities, it will not attain its full potential if research does not point to unresolved areas of tensions between groups. The purpose of this study is to conduct a narrative policy analysis during the implementation phase of the AODA to identify the parallel and divergent stories that surround this legislation. Using an interpretive stance, this study will be conducted with key people who have played a role in implementing the AODA in the municipal recreation context. Participants will include both persons responsible for implementing the AODA (i.e. city employees) and people with disabilities who have been directly affected by this policy. A conceptual framework
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".