Elements of caption quality: determining the priority of qualitative closed caption elements
Bibliographic record
Abstract
In Canada, the Canadian Radio Television and Communications Commission requires that all English- and French-language broadcasters caption 100% of their programs, and that live-produced programming – such as news broadcasts, sports events, and award shows – arecaptioned with a 95% accuracy rate for English-language. However, measuring caption quality as a purely objective count of the number of errors in the text means that many qualitative factors of quality are not considered. This research explored what priority Deaf and hard of hearing viewers place on non-quantitative elements of caption quality, namely caption display speed, missing words, spelling and grammar errors, and speaker identification. Using a survey tool based off the principals of the NASA-TLX workload assessment tool, participants were asked to watch two television clips with their original live-produced captions and provide feedback on how the captions impacted their viewing pleasure. The main findings suggested that viewers place equal value on verbatim accuracy and caption display speed, and that a trade off between the two cannot easily be made. This research provides a starting point for measuring caption quality using subjective quality factors.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".