Does narrator variability facilitate incidental word learning in the classroom?
Bibliographic record
Abstract
Recent studies have revealed that presenting novel words across various contexts (i.e., contextual diversity) helps to consolidate the meaning of these words both in adults and children. This effect has been typically explained in terms of semantic distinctiveness (e.g., Semantic Distinctiveness Model, Jones et al., Canadian Journal of Experimental Psychology, 66(2), 115, 2012). However, the relative influence of other, non-semantic, elements of the context is still unclear. In this study, we examined whether incidental learning of new words in children was facilitated when the words were uttered by several individuals rather than when they were uttered by the same individual. In the learning phase, the to-be-learned words were presented through audible fables recorded either by the same voice (low diversity) or by different voices (high diversity). Subsequently, word learning was assessed through two orthographic and semantic integration tasks. Results showed that words uttered by different voices were learned better than those uttered by the same voice. Thus, the benefits of contextual diversity in word learning extend beyond semantic differences among contexts; they also benefit from perceptual differences among contexts.
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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 | 0.001 | 0.009 |
| 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 | 0.000 |
| 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 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".