Let's Talk About Maths: The Role of Observed “Maths‐Talk” and Maths Provisions in Preschoolers' Numeracy
Bibliographic record
Abstract
ABSTRACT Developmental cognitive neuroscience highlights the importance of interactions between children and their environment. As young children spend increasing time in childcare, it is key to investigate the impact of “maths‐talk” and maths provisions in preschools. Qualitative insights from early educators indicate a greater bias toward counting activities than would be expected given the Early Years curriculum. In addition, we quantified the observed breadth of preschool practitioners' maths language (e.g., place‐value language), setting‐based maths provisions (e.g., quality of maths‐related activities), and their relation with children's early numeracy skills. In settings with greater practitioners' breadth of maths language, children display greater cardinality skills although our data call for the further investigation of parental socioeconomic status and education. We conclude with a discussion on the need to operationalize children's maths learning environments as diversely as possible. Enriching practitioners' skill sets may be an effective and needed way of improving early maths outcomes.
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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 | 0.003 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".