MétaCan
Menu
Back to cohort
Record W7100817420

Narrative Policy Analysis of the Accessibility for Ontarians With Disabilities Act in the municipal recreation context

2015· article· en· W7100817420 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicInfectious Aortic and Vascular Conditions
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationRecreationNarrativeContext (archaeology)Policy analysisNarrative inquiryLegislatureProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.361
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicInfectious Aortic and Vascular ConditionsFrench-language works237,207