The effects of enhancing L2 multiword items in captions: An approximate replication of Majuddin, Siyanova-Chanturia, and Boers (2021)
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 Studies investigating the acquisition of multiword items (MWIs) from reading have furnished evidence that the likelihood of acquisition improves considerably if such items are typographically enhanced (e.g., bolded or underlined) in the texts. In the case of captioned audio-visual materials, however, an earlier study by the authors did not find such compelling evidence. In that study, indications of an effect emerged only when the same video was watched twice. Arguably, for learners to benefit more immediately from typographic enhancement in captions, they may need to be made aware of its purpose beforehand. The present article therefore reports an approximate replication of Majuddin et al. (2021), but this time the students were informed about the MWI-learning purpose of watching the video. As in the original study, the learners watched a video once or twice with standard captions, with captions in which MWIs were enhanced, or without captions. The positive effect of enhancement for MWI learning was clearer than in the original study, and it already emerged after a single viewing. On the downside, enhancement was found to have a negative effect on lower-proficiency learners' comprehension of the content of the video.
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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 | 0.001 |
| 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) | 0.000 | 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 it