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Record W2009986646 · doi:10.7202/009350ar

Audesc: Translating Images into Words for Spanish Visually Impaired People

2004· article· en· W2009986646 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMeta Journal des traducteurs · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsnot available
Fundersnot available
KeywordsVisually impairedField (mathematics)MultimodalityLinguisticsMovie theaterComputer sciencePsychologyVisual artsArtHuman–computer interactionWorld Wide WebMathematics

Abstract

fetched live from OpenAlex

Although audiovisual translation is a relatively new field within Translation Studies, it is widening its perspectives to recent areas. Some of them are particularly concerned with minority groups, such as sensory impaired people. Specifically, the blind and visually impaired constitute an unexplored group. In this paper we introduce the system of “audio description,” which translates images into words to make audiovisual products accessible to this special-needs social sector. Since not much literature on the topic is available, we will provide the background and some general procedures for this type of intersemiotic translation. However, our greatest interest will be Audesc , the Spanish audio descriptive project developed by ONCE (the Spanish Organisation for the Blind), mainly applied to the cinema and the theatre. Finally, our paper hints at attaching the audio describer’s role to the audiovisual translator’s.

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.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.560
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.282
Teacher spread0.229 · 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