Enhancing <scp>L</scp> 2 vocabulary acquisition through implicit reading support cues in e‐books
Bibliographic record
Abstract
Abstract Various explicit reading support cues, such as gloss, QR codes and hypertext annotation, have been embedded in e‐books designed specifically for fostering various aspects of language development. However, explicit visual cues are not always reliably perceived as salient or effective by language learners. The current study explored the efficacy of implicit reading support cues—cues that are imperceptible to second‐language ( L 2) readers during their L 2 digital reading—for promoting L 2 vocabulary acquisition. Results suggest that subliminal formal priming—being one type of implicit reading support cues—helped L 2 readers significantly improve their form‐meaning vocabulary knowledge through e‐book reading. In particular, subliminal formal priming was more effective when the digital content, including the text and relevant illustration, was presented to L 2 readers simultaneously, rather than incrementally. The results have important implications vis‐à‐vis the need for the inclusion of implicit reading cues, and the optimal digital input presentation mode for enhancing L 2 vocabulary gains.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".