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Record W2077835807 · doi:10.3928/15394492-20131118-01

Hollywood Takes on Intellectual/Developmental Disability: Cinematic Representations of Occupational Participation

2013· article· en· W2077835807 on OpenAlexaff
Rebecca Renwick, Ann Fudge Schormans, Deborah Shore

Bibliographic record

VenueOTJR Occupational Therapy Journal of Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHollywoodGrounded theoryQualitative researchPerceptionOccupational therapyContext (archaeology)Intellectual disabilityPsychologyFilm industrySociologyPublic relationsSocial scienceMovie theaterPolitical scienceVisual artsArtHistory

Abstract

fetched live from OpenAlex

Adults with intellectual/developmental disability (IDD), and their occupational participation, are vastly under-represented in Hollywood films. Because films often provide individuals' only experience of people with IDD, cinematic representations can influence audience perceptions. Thus, films can help inform public perceptions about desired and appropriate occupational participation for people with IDD, potentially impacting their access to meaningful occupational participation and achievement of occupational potential. Accordingly, this research examined occupational portrayals of adults with IDD in contemporary Hollywood films. Occupational portrayals, as defined here, refer to representations of the dynamic process of the person participating in occupation(s) in a context. Grounded theory methods guided coding and analysis of qualitative data collected from eight contemporary films using an occupation-focused tool. Two major, striking themes emerging from the qualitative analysis--infantilization and simplification of participation in complex occupations (with three associated sub-themes)--are discussed. Implications of the findings and future research directions are considered.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.367
GPT teacher head0.542
Teacher spread0.175 · 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.

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

Citations15
Published2013
Admission routes1
Has abstractyes

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