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Record W7130389764 · doi:10.7202/1123400ar

Tracking Deaf Aesthetics in Deaf Spaces

2025· article· en· W7130389764 on OpenAlexaffvenueabout
Joanne Weber, Thurga Kanagasekarampillai, Connor Yuzwenko-Martin, Chris Dodd, Crystal Jones

Bibliographic record

VenuePerformance Matters · 2025
Typearticle
Languageen
FieldPsychology
TopicHearing Impairment and Communication
Canadian institutionsCanadian Association for the Study of the LiverUniversity of Alberta
Fundersnot available
KeywordsDramaturgyMeaning (existential)Reading (process)Scripting languageEthnographyFeelingDanceSet (abstract data type)

Abstract

fetched live from OpenAlex

Dramaturgy for plays featuring scripts developed with deaf actors, or by deaf playwrights, brings a layered and complex set of considerations about increasing accessibility in theatre productions for the cast, crew, and audience. These considerations include (1) making deliberate decisions about the languages the actors use; (2) addressing linguistic repertoires of the audience; (3) supporting or expanding sensory repertoires; (4) discussing the role of technology; and (5) incorporating deaf themes that are rarely known or understood by nondeaf audiences. Using a deaf aesthetics theoretical lens, the article explores dramaturgical decisions made by deaf playwrights, directors, and performers regarding four theatre and dance theatre works that were produced or are in progress in midwestern Canada between 2020 and 2025. The article also provides a performance ethnography of a staged reading for a script in progress developed by a deaf-led team of actors, a director, and a playwright. The performance ethnography relies on data collected from multiple sources: imagination-based activities resulting in artwork; steps within an adapted playbuilding model; video recordings of workshop sessions; the devised script; a video of the staged reading; and interviews with the deaf research team and participants. The data indicates that deaf aesthetics theatre practices drive accessibility strategies which are interwoven into the script. Plain Language Abstract (adapted by Kelsie Acton with Daniel Foulds) Dramaturgy is a way of making meaning and making people feel. People might see, hear, smell, or feel performance. So, dramaturgy is about making meaning and feeling through the senses. Dramaturgs are the people responsible for dramaturgy. When dramaturgs work on plays with deaf actors or with deaf writers they need to think about access. They must think about: The language the actors use. Will the actors speak or sign? The language the audience uses. Do they speak or sign? How many other languages do they use? How to help the audience use more than one sense. Will the play ask the audience to see as much as listen? How to use technology to increase access. How to explain the deaf experience to nondeaf audiences. This writing talks about four plays from midwestern Canada. These plays all took place in the last five years. We study a reading of a script a deaf playwright is working on with a team that is led by deaf people. We look at art made to develop the script, video of workshops, the script, video of a reading, and interviews with the deaf researchers and artists. We think about these things with deaf aesthetics theory. Deaf aesthetics theory says that deaf people should teach and make art for other deaf people. It also says art for deaf people should think about seeing and touching more than hearing. We found that deaf theatre makers don’t add access. Deaf theatre makers put access in the script from the start.

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)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0050.006
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.024
GPT teacher head0.322
Teacher spread0.298 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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