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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".