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Record W4394818042 · doi:10.5539/jel.v13n4p76

Assessment of the Physical Literacy Environment in Early Childhood Classrooms

2024· article· en· W4394818042 on OpenAlexaffvenue
J.M. Lachapelle, Annie Charron, Hélène Beaudry

Bibliographic record

VenueJournal of Education and Learning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversité du Québec à RimouskiUniversité du Québec à Montréal
Fundersnot available
KeywordsLiteracyVariety (cybernetics)PsychologyDisadvantagedEarly childhoodReading (process)Mathematics educationPedagogyEarly childhood educationDevelopmental psychologyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

This study aims to assess the physical literacy environment in 30 early childhood classrooms servicing 4- to 6-year-old children. A high-quality literacy environment that includes a variety of materials and resources is an important part of children’s emergent literacy, as research shows their use supports oral and written language development (Dynia et al., 2018; Yang et al., 2023). Observations were conducted using the Early Language and Literacy Classroom Observation Pre-K (ELLCO Pre-K; Smith et al., 2008) and the Literacy Environment Checklist (Smith et al., 2002), along with qualitative observational data and photographs of the classrooms. Overall, results show a low or basic level of quality of the physical literacy environment. Classrooms lack quality features such as a wide variety of books, writing materials in learning centers, accessible environmental prints, and representations of children’s diversity in reading materials. This level of quality is not considered sufficient to adequately support the language development of 4- to 6-year-old children, particularly those from disadvantaged backgrounds (Cunningham, 2010). These findings underline the importance of teachers’ professional development to better support emergent literacy through the physical environment of early childhood classrooms.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.634
Threshold uncertainty score0.121

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.316
Teacher spread0.308 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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