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Public Attitudes Towards Individuals with Intellectual Disabilities as Measured by the Concept of Social Distance

2009· article· en· W2158499153 on OpenAlexaff
Hélène Ouellette‐Kuntz, Philip Burge, Hilary K. Brown, E. Arsenault

Bibliographic record

VenueJournal of Applied Research in Intellectual Disabilities · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInclusion and Disability in Education and Sport
Canadian institutionsQueen's University
Fundersnot available
KeywordsPsychologySocial distanceIntellectual disabilityConstruct (python library)Sample (material)Scale (ratio)Social psychologyInclusion (mineral)Social acceptanceDevelopmental psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Background While current practices strive to include individuals with intellectual disabilities in community opportunities, stigmatizing attitudes held by the public can be a barrier to achieving true social inclusion. Methods A sample of 625 community members completed the Social Distance Subscale of the Multidimensional Attitude Scale on Mental Retardation. Results Older and less educated participants held attitudes that reflected greater social distance. Participants who had a close family member with an intellectual disability and those who perceived the average level of disability to be ‘mild’ expressed less social distance. The limited variability in scores leads us to question our overall finding of very favourable attitudes towards social interaction with persons with intellectual disabilities. Conclusions This study demonstrates that although certain demographic variables are still relevant in identifying social distance attitudes, the measurement of this construct requires revision to ensure a valid and sensitive reflection of the public’s attitudes.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.138
GPT teacher head0.412
Teacher spread0.274 · 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 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

Citations160
Published2009
Admission routes1
Has abstractyes

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