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Record W4232525089 · doi:10.32920/14637930.v1

"Suit the Action to the Word, the Word to the Action": An Unconventional Approach to Describing Shakespeare's Hamlet

2021· preprint· en· W4232525089 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
KeywordsEntertainmentDanceAction (physics)ParagraphDisadvantageThe artsHAMLET (protein complex)PsychologyAdvertisingSociologyVisual artsMedia studiesMultimediaArtComputer sciencePolitical scienceLawLiteratureBusiness

Abstract

fetched live from OpenAlex

<p>[First paragraph]: "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 entertainment 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."</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0030.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.289
GPT teacher head0.319
Teacher spread0.030 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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