Impacts on quality: Enjoyment factors in blind and low vision audience entertainment ratings: A qualitative study
Bibliographic record
Abstract
Audio description (AD) is one of the main methods that people who are blind or low vision (B/LV) use to access film, television, and theatre content. AD is a second audio track inserted into the space(s) where speech is absent, which tends to be only a few seconds. Contained in that second track is an audio description of the important visual information contained within a specific scene. However, as there is insufficient time to describe all visual information, decisions about what is important to describe and how to present that information (style) to optimize a B/LV viewer's entertainment experience are required. Most research to date has considered only short-term, single-episode experiences to gauge viewers' reactions to the AD content. In addition, this research typically has used a monotone, single style of audio description, which is defined as "the conventional style" in this paper. We use an integrative style instead, that is defined as 'AD designed to fit a specific show", and differed between shows. We carried out a within-subjects longitudinal study with eight episodes of a dark comedy, using different description styles and describers in order to assess viewer engagement and preferences for AD describer style, language use, timing, and fit to the show. Twenty-four blind participants viewed and rated all eight episodes. Major findings included that most participants found the integrative style entertaining, a fit with the specific episodes, and enjoyable. Some participants, however, preferred the conventional style and struggled with the language and topic of a dark comedy and its associated descriptions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".