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Record W4248678766 · doi:10.32920/ryerson.14645652

Children's perspectives on outdoor play programs in childcare centre playgrounds: are early childhood educators listening?

2021· preprint· en· W4248678766 on OpenAlexaff
Yunjoo Lee

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicOutdoor and Experiential Education
Canadian institutionsToronto Metropolitan UniversityEducation and Early Childhood Development
Fundersnot available
KeywordsViewpointsActive listeningEarly childhoodMosaicPsychologyQualitative researchPedagogyEarly childhood educationDevelopmental psychologySociologyGeographySocial scienceCommunication

Abstract

fetched live from OpenAlex

Outdoor programs are part of children’s everyday experiences in childcare centres. However, there is a lack of research that explores children’s viewpoints on their outdoor programs in childcare centres. This qualitative study examined children’s perspectives using the Mosaic approach. In addition, the early childhood educators (ECEs) were interviewed to investigate how they learn about and support children’s interests. Findings indicate that using the Mosaic approach can contribute to a more holistic understanding of children’s perspectives of their outdoor play programs. Findings also indicate that ECEs use observation and communication to learn about children’s interests. The ECEs also stated that they support children’s interests during their outdoor programs through verbal support and modelling, changing and expanding activities, and preparing various activities and materials for the children. Discussion on the findings explores multiple methods for tapping children’s perspectives, implications for teacher practices, and direction for future research.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.293
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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