Bibliographic record
Abstract
Readers familiar with research into the value of captions and/or subtitles for language learning may wonder what new there is to say when there are many hundreds of studies since 1993 reported on Günter Burger’s website (Burger 2025). This edited collection shows that there is still a lot more to be said (and done). For those less familiar with the field, the core message is that same-language subtitles (or (closed) captions as they are usually known) intended primarily for the deaf and hard-of-hearing also make films and TV programmes accessible to hearing second/foreign language viewers and may help learners/viewers understand the language and content of what they are watching and acquire viewing and listening skills in the foreign language along with vocabulary. In this review, captions always refer to same-language subtitles, while subtitles translate dialogue from another language. In the global context of research into captioned viewing and language learning, three main centres have emerged in the last ten to fifteen years: in Flanders (Leuven/Ghent), Hong Kong/Macao, and Barcelona, with many other researchers spread all over the world. The collection under review comes from the GRAL language acquisition research group at the University of Barcelona, coordinated by Carmen Muñoz.
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 | 0.001 | 0.008 |
| 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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 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".