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Record W3045642252 · doi:10.7202/1070536ar

Defining subjectivity in visual art audio description

2020· article· en· W3045642252 on OpenAlexvenueno aff
Silvia Soler Gallego

Bibliographic record

VenueMeta Journal des traducteurs · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
FundersUniversidad de GranadaColorado State University
KeywordsSubjectivityCategorizationAudio visualModality (human–computer interaction)Computer scienceLinguisticsComposition (language)Cognitive psychologyPsychologyMultimediaHuman–computer interactionArtificial intelligenceEpistemology

Abstract

fetched live from OpenAlex

Audio description is an intersemiotic translation modality used to make art museums accessible to visually impaired visitors. Existing audio description guidelines in various countries recommend describing only that which is seen, in other words, to avoid subjective interpretations of the visual message. However, there is evidence that some visually impaired people prefer more subjective audio descriptions. The controversy around this issue has generated reception- and product-oriented studies of audio description which demonstrate that not only is subjectivity present in existing audio descriptions, but also that it may benefit the construction of a more meaningful experience. A methodology which combines corpus and contextual analysis and draws on cognitive linguistics as well as art theories has been followed in this study to examine audio descriptive guides in art museums in four different countries. Results show considerable levels of subjectivity and offer a categorization of this element. Additionally, the level and type of subjectivity appear to be influenced by contextual factors, including the level of abstraction of the artwork and the audio describer’s degree of compliance with existing guidelines.

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)0.000
metaresearch head score (Gemma)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.890
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.266
Teacher spread0.184 · 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 designNot applicable
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

Citations12
Published2020
Admission routes1
Has abstractyes

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