Reading to Children and Listening to Children Read: Mother–Child Interactions as a Function of Principal Reader
Bibliographic record
Abstract
Research Findings: Although storybook reading has received considerable research attention, listening to children read has been the source of much less inquiry. In this study, 40 mother–child dyads were videotaped during adult-to-child and child-to-adult reading. Relations between book-related themes (e.g., types of talk), maternal evaluative feedback (e.g., praise, criticism), maternal miscue feedback (e.g., graphophonemic clues, terminal feedback), and child engagement (e.g., laughter, questions) were analyzed. The results suggest that the development of literacy appreciation and literacy skill can occur during the same storybook-reading session. Specifically, when mothers read to their children, communication about the illustrations was associated with increased child engagement, yet a positive correlation was also observed between text-related productions and child engagement. When children read to their mothers, text-related productions were featured more prominently. After children made reading errors (miscues), graphophonemic and terminal feedback were the 2 most frequent responses by mothers. In addition, graphophonemic cues were positively associated with child engagement. Practice or Policy: In sum, the results demonstrate that adult-to-child and child-to-adult reading serve the goals of both literacy acquisition training and literacy appreciation; furthermore, orienting children toward the text during either session did not hamper child engagement.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".