The Rogue Poster-Children of Universal Design: Closed Captioning and Audio Description
Bibliographic record
Abstract
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 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.044 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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.
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".