Reading to bilingual preschoolers: An experimental study of two book formats
Bibliographic record
Abstract
Abstract Reading stories to children provides opportunities for word learning. Bilingual children, however, encounter new words in each of their languages during shared storybook reading, and the way in which these words are presented can vary. We compared learning from two types of bilingual book materials: single‐language books and bilingual books. Five‐year‐old English‐French bilinguals (n = 67) were randomly assigned to hear an original story from a balanced bilingual experimenter in one of the two book formats. Children's learning of English and French labels for five novel objects embedded in the story was assessed via a pointing task. Children were successful at learning words in both languages, and performance was not affected by book format nor children's language proficiency. These results suggest that children are flexible word learners and that shared book reading – regardless of book format – is an effective way to teach bilingual children new words in two languages. Highlights In a shared storybook reading task, bilingual 5‐year‐olds encountered new words in two languages via single‐language or bilingual books. Word learning from the two book formats was compared. Both formats supported word learning, regardless of children's language proficiency. Bilingual children are flexible word learners, and shared book reading – regardless of book format – supports bilingual literacy development.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".