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Record W2116764178 · doi:10.1177/0145482x0910300307

“Suit the Action to the Word, the Word to the Action”: An Unconventional Approach to Describing Shakespeare's <i>Hamlet</i>

2009· article· en· W2116764178 on OpenAlexaff
John Patrick Udo, Deborah I. Fels

Bibliographic record

VenueJournal of Visual Impairment & Blindness · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEntertainmentDanceAction (physics)PsychologyMultimediaSociologyVisual artsComputer scienceArt

Abstract

fetched live from OpenAlex

Without access to audio description, individuals who are visually impaired (that is, are blind or have low vision) may be at a unique social disadvantage because they are unable to participate fully in a culture that is based on and heavily saturated by the enjoyment of audiovisual entertainments (Packer & Kirchner, 1997). Audio description was introduced as an adaptive after-the-fact strategy to give individuals who are visually impaired better access to media (Fels, Udo, Ting, Diamond, & Diamond, 2006). With audio description, visually important elements of the entertainment experience are described during pauses in the dialogue (Packer & Kirchner, 1997). Conventional audio description practices, as outlined by Snyder (2005, 2007), have been adopted as an access strategy for live theater, television, and film, although little research has informed these practices (Gerber, 2007). Alternative audio description strategies are also being explored and developed, mainly by theater (for example, British Council for the Arts, 2007; Graeae Theatre Company, n.d.) or dance troupes (for example, CandoCo Dance Company, 2008; StopGap, 2008) whose mandates are focused on the inclusion of individuals with disabilities in their casts. We believe that audio description is a creative process, and from working firsthand on live audio description projects we learned how cognitively demanding live description is on a describer. We recognize that audio describers are generally volunteers and have a genuine interest in making the theater accessible to individuals with visual impairments. In this article, we present a summary of conventional and alternative audio description practices. We discuss a case account of an alternative audio description strategy that was prepared for a live production of Hamlet using the subjective, emotional style proposed by Fels, Udo, Diamond, and Diamond (2006) and Fels, Udo, Ting et al. (2006); and used by Udo and Fels (in press). CONVENTIONAL AUDIO DESCRIPTION Formal audio description is a relatively new media access technology compared with others, such as closed captioning. Audio description has existed for many years, however, as an ad hoc and access strategy in which friends and family members acted as informal describers, whispering descriptions to their friends and relatives who were visually impaired (Packer & Kirchner, 1997). In the 1970s, Gregory Frazier formalized audio description by defining formal procedures and processes for the description of media and cultural events (Snyder, 2005). In the 1990s, WGBH Radio & Television began offering a Descriptive Video Service as part of its broadcasts (WGBH Educational Foundation, n.d.). Numerous theater groups in the United States and the United Kingdom have also embraced these technologies and techniques for live theater. In 1981, Margaret Pfanstiehl and Cody Pfanstiehl were among the pioneers to develop audio description techniques for live theater. Additional guidelines for the creation of audio description for television, film, and live theater exist (Audetel Consortium, 2000; Audio Description International, 2005). However, most of these guidelines tend to rely heavily on anecdotal evidence and the historical use of a specific process, rather than one validated by published research. Pfanstiehl and Pfanstiehl (quoted in The Play's the Thing, 1985, p. 91) cautioned describers not [to] evaluate or interpret, but rather be like the faithful lens of a camera and encouraged them to describe visual elements without drawing assumptions for the listener. In doing so, the describer must interpret and translate important visual action or stimuli (costumes, setting, lighting, and so forth) as objectively as possible. Since limited time is available to describe visual stimuli, describers must use precise and highly descriptive language. There has been limited research on the effectiveness and impact of live audio description on audiences. …

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)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0090.027
Scholarly communication0.0070.009
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.003

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 head0.151
GPT teacher head0.345
Teacher spread0.194 · 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 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

Citations54
Published2009
Admission routes1
Has abstractyes

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