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Record W4411322564 · doi:10.1177/14687984251351202

Making numeracy and literacy learning visible in play-based publicly funded programs

2025· article· en· W4411322564 on OpenAlexafffundabout
Christine McLean, Randi Cummings, Lyse Anne LeBlanc, Anne Briscombe, Dina Mohamed, Jessie‐Lee D. McIsaac

Bibliographic record

VenueJournal of Early Childhood Literacy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsMount Saint Vincent University
FundersCanada Research Chairs
KeywordsNumeracyLiteracyMathematics educationComputer sciencePsychologyPedagogy

Abstract

fetched live from OpenAlex

There is an increased movement toward locating early childhood programs within school environments. However, there remains some tension between long-held perceptions of play and the realities of play-based pedagogies, resulting in increasing pressure for evidence of outcomes in academic skills, such as numeracy and literacy. This study seeks to provide pedagogical examples of how numeracy and literacy skills are operationalized and supported within a play-based early learning environment in Nova Scotia (Canada). Using a photo elicitation methodology, 17 early childhood educators working in Pre-primary Programs with children aged 4-5 years old participated in a series of six virtual focus groups that included information sharing, discussions of photos of the participants learning environments, and participant-led analysis. Participants across all groups shared photo examples of numeracy and literacy learning occurring during child-initiated play and discussed their perceptions of what supported and hindered their ability to support numeracy and literacy learning during play and their ability to share these beliefs and observations. The results provided a range of rich and diverse examples of numeracy and literacy learning through play and the crucial role of the early childhood educator within the context of the school-based early childhood programs.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.370
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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