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Record W7124185077 · doi:10.1080/23266988.2025.2576460

Inclusion of Individuals With Intellectual and Developmental Disabilities at Museums, Aquariums, Zoos, and Science Centers in Canada

2025· article· en· W7124185077 on OpenAlexafffundabout
Julia M. Ranieri, Gemma Graziosi, Nicole Neil, Anton Puvirajah

Bibliographic record

VenueInclusion · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInclusion (mineral)Intellectual disabilityQualitative researchEthnic group

Abstract

fetched live from OpenAlex

This study explored the facilitators and barriers individuals with intellectual and developmental disabilities (IDD) encounter at museums, aquariums, zoos, and science centers (MAZSC). Ten staff members from MAZSC across Canada participated in semistructured interviews. Eighteen facilitators and 15 barriers to participation and inclusion in MAZSC were identified at the administrative, staff, environmental, and visitor levels. Environmental factors were most frequently identified as facilitators of inclusion, whereas administrative factors were most frequently identified as barriers. The interviews revealed that although progress has been made to improve opportunities for inclusion and participation for individuals with IDD, barriers to participation and inclusion continue to exist. Findings from this study can inform the continued development of inclusive practices and policies in MAZSC.

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)0.002
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0170.006
Scholarly communication0.0030.001
Open science0.0020.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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