Saying “Thank You” for Quality Closed Captions: A Promising Shift in Inviting Access
Bibliographic record
Abstract
Even as deaf and hard of hearing filmmakers and activists repeatedly call for quality captions on all video content, many non-deaf filmmakers have managed to remain unaware of the need for and purpose of captions. Implicit biases drive many filmmakers to exclude access from their budgets and their films. These biases include a notion that caption users are not a viable audience, concerns that captions will threaten the beauty of video images by covering part of the screen, and an audist attitude that any level and quality of transcription of spoken dialogue must be adequate. The author is a hearing captioner and filmmaker. In this essay, she reflects on how she advocates for film accessibility through captions. She describes her strategy, how she frames “onscreen real estate,” and responses from filmmakers for captions, including the hopeful way that some say thank you. Quality captions are contrasted against woefully inadequate captions—or “craptions”—provided automatically by YouTube and by companies with cut-rate services. The author considers a focus on inviting access rather than waiting for a compliance-based method of only captioning a film when the filmmaker learns it is required.
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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".