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Record W4406938345 · doi:10.1177/14639491241311650

Play–literacy interface in childhood education: Across the scales of time and space

2025· article· en· W4406938345 on OpenAlexafffund
Chunhong Liu, Qinghua Chen, Limin Zhang, Angel M. Y. Lin

Bibliographic record

VenueContemporary Issues in Early Childhood · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEarly childhood educationLiteracyEarly childhoodInterface (matter)Space (punctuation)PsychologyDevelopmental psychologySociologyPedagogyPhysicsComputer science

Abstract

fetched live from OpenAlex

Play has been widely examined and viewed as an effective pedagogy to promote children's literacy development. While current research has identified various benefits associated with play, discrepancies still exist regarding what type of play could be more effective, for example, home play or school play. This could further lead to an outcome-oriented, positivist perception of play as a designed intervention that risks neglecting children's agency in creating, negotiating, and interpreting their play activities. In this conceptual article, we explore the theoretical affordance of the concept of temporal and spatial scales to rethink children's play as a dynamic, multilayered flow of meaning-making and negotiation across time and space. We discuss how using the lens of scales, play can be analyzed as actions, processes, and contexts. The methodological and pedagogical implications of scales are also illustrated for practical applications in research and education on play-based literacy learning. Finally, this article aims to draw researchers and educators’ attention to the complexity and diversity of play, as well as children's agency in (re)constructing playfulness.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.555

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.308
Teacher spread0.300 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2025
Admission routes2
Has abstractyes

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