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Record W4306408296 · doi:10.1386/public_00127_1

The Language of Blindness and its Rapport with Sight: Immersive Descriptive Audio and Rainbow On Mars

2022· article· en· W4306408296 on OpenAlexaffabout
Devon Healey

Bibliographic record

VenuePublic · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSightBlindnessClosenessPrivilege (computing)PsychologyRainbowVisual artsComputer scienceArtOptometryComputer securityMedicineAstronomy

Abstract

fetched live from OpenAlex

Rapport defines a certain kind of relationship, one that is not close. Rapport, in other words, defines the closeness and relatability that a relationship should have. This article engages the question, can blindness and sight develop a rapport? It is this question of rapport that animated the 2021 Pop-Up workshop of my play, Rainbow on Mars with Toronto immersive theatre company, Outside the March. The question of building a rapport was not only asked of the entities/characters Blind and Sight but also of the theatre itself and of its audiences. How might blindness and sight come together in the theatre in such a way that does not simulate blindness (blind folds, absence of stage lighting, etc.) nor privilege sight (audio description through headsets for blind patrons only)? This article explores the building of rapport between blindness and sight through the play Rainbow on Mars and the development of, what I call, Immersive Descriptive Audio.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0060.017
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.048
GPT teacher head0.219
Teacher spread0.171 · 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 designTheoretical or conceptual
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
Published2022
Admission routes2
Has abstractyes

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