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Record W3210157774 · doi:10.1007/s43545-021-00266-8

Profiles of teacher–child interaction quality in groups of 3-year-old children in Quebec and France

2021· article· en· W3210157774 on OpenAlexafffundabout
Maude Roy-Vallières, Nathalie Bigras, Annie Charron, Caroline Bouchard, Andréanne Gagné, Philippe Dessus

Bibliographic record

VenueSN Social Sciences · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsContext (archaeology)Quality (philosophy)Sociocultural evolutionPsychologyDevelopmental psychologyLatent class modelDemographyGeographySociologyStatisticsPhysicsMathematics

Abstract

fetched live from OpenAlex

Theory and studies support that educational quality may differ according to socio-political context even in states with similar cultures. Based on a secondary analysis of data, this study aims at identifying latent profiles of adult-child interaction quality in groups of three-year-old children in Quebec's (Canada) early childhood centers and France's kindergarten classrooms using the CLASS Pre-K. This study also aims to explore existing associations between identified profiles, socio-political contexts, and structural characteristics (staff qualifications, ages, group size). Latent profile analyses showed four interaction quality profiles, namely a high-quality profile (HQ), a medium-high-quality profile (MHQ), a medium quality profile (MQ), and a medium-low-quality profile (MLQ). The scores of the three CLASS Pre-K domains associated with identified profiles show a higher average interaction quality in Quebec compared with France, suggesting a more favorable sociocultural context for interaction quality in Quebec. As for characteristics of structural quality, analyses suggest that the group size variable is significantly associated with scores of interaction quality, with the HQ and the MHQ profiles showing a significantly lower group size than the MQ and MLQ profiles. Age is also significantly associated with profiles, exhibiting a general trend of younger participants found in higher quality profiles. Courses of action to enhance French policies are discussed.

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 score0.160
Threshold uncertainty score0.883

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 communication0.0000.000
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 head0.026
GPT teacher head0.357
Teacher spread0.331 · 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

Citations4
Published2021
Admission routes3
Has abstractyes

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