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Record W4247430172 · doi:10.32920/14638143

The Rogue Poster-Children of Universal Design: Closed Captioning and Audio Description

2021· preprint· en· W4247430172 on OpenAlexaff
John-Patrick Udo, Deborah I. Fels

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsClosed captioningComputer scienceProcess (computing)Set (abstract data type)Service (business)Control (management)MultimediaArtificial intelligenceImage (mathematics)Programming languageBusiness

Abstract

fetched live from OpenAlex

To abide by the tenants of universal design theory, the design of a product or service needs to not only consider the inclusion of as many potential users and uses as possible but also do so from conception. Control over the creation and adaption of the design should, therefore, fall under the purview of the original designer. Closed captioning has always been touted as an excellent example of an design or electronic curb-cut because it is a system designed for people who are deaf or hard of hearing, yet is used by many others for access to television in noisy environments such as gyms or pubs, or to learn a second language. Audio description is poised to have a similar image. In this paper, we will demonstrate how the processes and practices associated within closed captioning and audio description, in their current form, violate some of the main principles of universal design and are thus not such good examples of it. In addition, we will introduce an alternative process and set of practices through which directors of television, film and live events are able to take control of closed captioning and audio description by integrating them into the production process. In doing so, we will demonstrate that closed captioning and audio description are worthy of directorial attention and creative input rather than being tacked on at the very end of the process and usually to only meet regulatory or legislative mandates.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.621

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 communication0.0000.000
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 head0.055
GPT teacher head0.229
Teacher spread0.174 · 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.

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

Citations7
Published2021
Admission routes1
Has abstractyes

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