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Record W3214680513 · doi:10.15173/cjae.v1i1.4982

Equity: What Model Should We Use When We Talk About Autism?

2021· article· en· W3214680513 on OpenAlexaffabout
Rebekah Kintzinger

Bibliographic record

VenueCanadian Journal of Autism Equity · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsAutism Canada
Fundersnot available
KeywordsSocial model of disabilityAutismInternational Classification of Functioning, Disability and HealthEquity (law)PsychologyMedical model of disabilityMovement (music)Public relationsPolitical scienceDevelopmental psychologyPsychiatryRehabilitationLaw

Abstract

fetched live from OpenAlex

In the Canadian disability rights movement, with regards to autism specifically, there has been a shift towards recognizing what is called a social model of disability. Through this movement, there has been a desire to incorporate that model into practice in governments, institutions, and healthcare. This desire also stems from advocate-centric and first-voice communities, where disabilities like autism are not viewed through a deficit-based lens. This article aims to discuss the often polarizing social and medical models of disability, comparing their uses in the disability world while weighing their respective benefits. Finally, an alternative model of disability that intersects these models is discussed as an alternative. This model is called the International Classification of Functioning, which recognizes three levels that impair a disabled person: the body, the person, and the environment. It is from this focus that policy can be developed to answer the calls of the pan-disability movement; to provide equitable changes across services and domains that are rightly deserved for Autistic and disabled people.

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)0.033
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.840
Threshold uncertainty score0.318

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.032
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0140.066
Scholarly communication0.0200.030
Open science0.0040.011
Research integrity0.0090.016
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.164
GPT teacher head0.383
Teacher spread0.219 · 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 designTheoretical or conceptual
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

Citations3
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Autism EquitySame topicDisability Rights and RepresentationFrench-language works237,207