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Record W2338012757 · doi:10.1145/1279540.1279551

Emotive captioning

2007· article· en· W2338012757 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComputers in entertainment · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsClosed captioningEmotiveProsodyComputer scienceCLIPSMultimediaStyle (visual arts)PsychologyLinguisticsSpeech recognitionArtificial intelligenceImage (mathematics)

Abstract

fetched live from OpenAlex

Television and film have become important equalization mechanisms for the dissemination and distribution of cultural materials. Closed captioning has allowed people who are deaf and hard of hearing to be included as audience members. However, some of the audio information such as music, sound effects, and speech prosody are not generally provided for in captioning. To include some of this information in closed captions, we generated graphical representations of the emotive information that is normally represented with nondialog sound. Eleven deaf and hard of hearing viewers watched two different video clips containing static and dynamic enhanced captions and compared them with conventional closed captions of the same clips. These viewers then provided verbal and written feedback regarding positive and negative aspects of the various captions. We found that hard of hearing viewers were significantly more positive about this style of captioning than deaf viewers and that some viewers believed that these augmentations were useful and enhanced their viewing experience.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.434

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)0.0000.000

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 head0.022
GPT teacher head0.245
Teacher spread0.222 · 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