Bibliographic record
Abstract
Subtitles for the deaf and hard of hearing (SDH), known as captions in the US, Canada and Australia, may take the form of intralingual or interlingual translation. They may be “burnt in (open) or superimposed (closed)”, “prepared beforehand or delivered live”, “provided in an edited or (near) verbatim form”, and provide access to verbal as well as non-verbal (acoustic) information. Research on SDH started in the US in the early 1970s with a series of reception studies, mostly doctoral theses that explored the benefits of captions for deaf students. Live subtitling is “the real-time transcription of spoken words, sound effects, relevant musical cues, and other relevant audio information” to enable deaf or hard-of-hearing persons to follow a live audiovisual programme. Since their introduction in the US and Europe in the early 1980s, live subtitles have been produced through different methods: standard QWERTY keyboards, dual keyboard, Velotype and the two most common approaches, namely stenography and respeaking.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.025 |
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".