Storybooks aren't just for fun: narrative and non-narrative picture books foster equal amounts of generic language during mother-toddler book sharing
Bibliographic record
Abstract
Parents and children encounter a variety of animals and objects in the early picture books they share, but little is known about how the context in which these entities are presented influences talk about them. The present study investigated how the presence or absence of a visual narrative context influences mothers' tendency to refer to animals as individual characters or as members of a kind when sharing picture books with their toddlers (mean age 21.3 months). Mother-child dyads shared both a narrative and a non-narrative book, each featuring six animals and matched in terms of length and quantity of text. Mothers made more specific (individual-referring) statements about animals in the narrative books, whereas they provided more labels for animals in the non-narrative books. But, of most interest, the frequency and proportion of mothers' use of generic (kind-referring) utterances did not differ across the two different types of books. Further coding of the content of the utterances revealed that mothers provided more story-specific descriptions of states and actions of the animals when sharing narrative books and more physical descriptions of animals when sharing non-narrative books. However, the two books did not differ in terms of their elicitation of natural facts about the animals. Overall, although the two types of books encouraged different types of talk from mothers, they stimulated generic language and talk about natural facts to an equal degree. Implications for learning from picture storybooks and book genre selection in classrooms and home reading are discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".