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Record W4387460302 · doi:10.1177/01650254231202705

A longitudinal study of school adjustment among children attending Reggio-inspired preschools

2023· article· en· W4387460302 on OpenAlexafffund
Barry H. Schneider, Mara Manetti, Nadia Rania, José M. Tomás, Amparo Oliver, Robert J. Coplan, Quinlan Taylor

Bibliographic record

VenueInternational Journal of Behavioral Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSocioeconomic statusPsychologyDevelopmental psychologyLongitudinal studyReceptive languageDemographyMedicineVocabularyPopulation

Abstract

fetched live from OpenAlex

The goal of this study was to track the progress of Italian children at risk for school failure enrolled in preschools based on the Reggio-Emilia approach. Risk factors considered included family socioeconomic status (SES), child receptive language, and child gender. Participants were 211 children ( M age = 60.8 months, 116 girls) in Reggio-inspired preschools in Genoa, Italy. The sample was followed over six time points starting from the last year of preschool (ages 5–6 years) through the end of the second year of elementary school. We examined trajectories of school liking, teacher–child relationships, and teacher-rated language/mathematics. Trajectories of at-risk children were predominantly indistinguishable from those of the full sample. Children at risk because of lower SES and poorer receptive language (but not gender) were rated by teachers as more dependent than children not identified as at risk. Contrary to expectations, children of mothers from low-SES backgrounds liked school more than the rest of the sample.

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 score0.031
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.389
Teacher spread0.314 · 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
Published2023
Admission routes2
Has abstractyes

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