Knowledge-Building Through Categorization: Boosting Children’s Vocabulary and Content Knowledge in a Shared Book Reading Program
Bibliographic record
Abstract
Research Findings: The purpose of this study was to examine the effects of categorization in shared book reading as a mechanism for developing preschoolers’ topical knowledge in life science. Prekindergarten children from 4 schools and 23 classrooms in a large metropolitan area were randomly selected into treatment (N = 12 classrooms) and control groups (N = 11 classrooms). In the 4-month trial, children in the treatment group were introduced to science topics that were structured to promote categorization and concepts through shared book reading of text-sets that included narrative nonfictional and information books, while the control group received the same materials without lessons on categorization. Pre- and posttests examined child outcomes in vocabulary, categorical properties, content and inferential reasoning. Results indicated that children in the treatment group learned significantly more words and made more explicit inferences than the control group. Policy and practice: Together, it highlights the potential use of categorization in knowledge-building and schema 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".