Does Mother Know Best? Maternal Knowledge Calibration Predicts Children’s Oral Language Development
Bibliographic record
Abstract
For young children, maternal testimony is an important source of knowledge. Research suggests that children privilege assertions expressed with certainty; however, adults frequently overestimate their knowledge, which may lead them to express certainty about incorrect information. This study addressed three questions. (1) To what extent do mothers convey domain knowledge when talking to their kindergartners? (2) Do mothers successfully calibrate their knowledge during these conversations? (3) Does mothers’ knowledge calibration predict their children’s language outcomes? Forty-nine mother-child dyads read a picture book about a familiar domain. Mothers’ assertions of domain knowledge were coded for accuracy and expressed certainty. Results revealed that mothers tended to overestimate their knowledge. Knowledge calibration accuracy positively predicted child outcomes. Successful calibration was associated with stronger vocabulary knowledge and listening comprehension, whereas poor knowledge calibration was associated with weaker child outcomes. Knowledge calibration may be a crucial factor in the successful transmission of knowledge during mother-child conversations and impact children’s language 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 | 0.002 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".