Examining incidental vocabulary acquisition from captioned video
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Previous comparisons of vocabulary uptake from captioned and uncaptioned audio-visual materials have almost consistently furnished evidence in favour of captioned materials. However, it is possible that many such comparative studies gave an advantage to the captioned input conditions by virtue of their use of written word prompts in the tests. The present study therefore examines whether aurally presented test prompts yield equally compelling evidence for the superiority of captioned over uncaptioned video. Intermediate EFL learners watched a ten-minute TED Talks video either with or without captions and were subsequently given a word recognition and a word meaning test, with half of the test prompts presented in print and the other half presented aurally. While the results of the word recognition test were inconclusive, the word meaning test yielded significantly better scores by the group that watched the captioned video. However, this was due entirely to their superior scores on the printed word prompts, not the aural ones. This suggests that evaluations of the benefits of captions for vocabulary acquisitions should take input-modality – test-modality congruency into account.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.005 | 0.001 |
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 it