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Record W4316814245 · doi:10.1080/09687599.2023.2165434

Reviewing the limitations of publicly funded adult developmental services in Ontario: exposing ableist assumptions within the administrative process

2023· article· en· W4316814245 on OpenAlexafffundabout
Isabella Chawrun

Bibliographic record

VenueDisability & Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHealthcare innovation and challenges
Canadian institutionsYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsChristian ministryProcess (computing)Field (mathematics)Public relationsIntellectual disabilityDisability studiesPolicy developmentFocus groupSociologyPublic administrationPsychologyPolitical scienceGender studiesLaw

Abstract

fetched live from OpenAlex

This paper considers the ways that publicly funded developmental services for adults with developmental disabilities in southern Ontario are limited in how they support clients. This paper is informed by field research conducted in the summer of 2019, which was composed of semi-structured interviews, focus groups, and a policy review. Informed by parent advocates who are the main caregivers of their adult children labelled with intellectual and developmental disabilities, this paper claims that the administrative processes of the Ontario ministry that manages and funds adult disability services relate to broader exclusionary patterns among adults with developmental disabilities. I explore this claim by reviewing how common ableist assumptions of people with developmental disabilities are ingrained in the policies and administrative processes of these services. I contribute to ongoing discussions among Critical Disability Scholars of the ways that disability as a social category can be articulated outside of ableist assumptions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.074
metaresearch head score (Gemma)0.102
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.898

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0740.102
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0260.042
Scholarly communication0.0180.011
Open science0.0050.011
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.303
GPT teacher head0.412
Teacher spread0.110 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2023
Admission routes3
Has abstractyes

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