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Record W4253508388 · doi:10.32920/ryerson.14638167.v1

Inclusive Design, Audio Description, and Diversity of Theatre Experiences

2021· preprint· en· W4253508388 on OpenAlexafffund
Margot Whitfield, Deborah I. Fels

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsToronto Metropolitan UniversityVictoria Park
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsNarrativePresentation (obstetrics)Diversity (politics)Process (computing)AestheticsInclusion (mineral)SociologyPsychologyMultimediaPedagogyPublic relationsComputer sciencePolitical scienceArtSocial psychologyLiterature

Abstract

fetched live from OpenAlex

The conventional approach to audio description (AD) uses third-person narrative, factual delivery style, post-planning, and third-party delivery, making it incompatible with inclusive design principles and equitable access to sensory stimuli. This paper discusses Clay & Paper Theatre’s alternative AD approach, involving actors, script writers, musicians and directors. With no previous exposure to inclusive design, the creative team developed the design process: script modification, characters and music integration, and sensory tour presentation. Innovative methodology taught actors and directors to think about accessibility from the start of their creative processes. Actors found the inclusive design process useful in developing a better understanding of character roles. Audience members enjoyed the play through the role of music and its link to the narrative and characterization. Clay & Paper Theatre’s alternative AD approach exemplifies social innovation in inclusive theatre design for blind and low vision (B/LV) audiences, with an emphasis on process and service outcomes.

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)0.004
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0090.005
Open science0.0010.013
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.074
GPT teacher head0.253
Teacher spread0.179 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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