Taxonomies Support Preschoolers’ Knowledge Acquisition from Storybooks
Bibliographic record
Abstract
For young children, storybooks may serve as especially valuable sources of new knowledge. While most research focuses on how extratextual comments influence knowledge acquisition, we propose that children’s learning may also be supported by the specific features of storybooks. More specifically, we propose that texts that invoke children’s knowledge of familiar taxonomic categories may support learning by providing a conceptual framework through which prior knowledge and new knowledge can be readily integrated. In this study, 60 5-year olds were read a storybook that either invoked their knowledge of a familiar taxonomic category (taxonomic storybook) or focused on a common thematic grouping (traditional storybook). Following the book-reading, children’s vocabulary acquisition, literal comprehension, and inferential comprehension were assessed. Children who were read the taxonomic storybook demonstrated greater acquisition of target vocabulary and comprehension of factual content than children who were read the traditional storybook. Inferential comprehension, however, did not differ across the two conditions. We argue for the importance of careful consideration of book features and storybook selection in order to provide children with every opportunity to gain the knowledge foundational for successful 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".