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Record W4409207660 · doi:10.1017/s0305000925000169

Modeling monolingual and bilingual children’s language attitudes towards variation in metropolitan France

2025· article· en· W4409207660 on OpenAlexaff
Anna Ghimenton, Christophe Coupé, Nelly Bonhomme, Jinke Song, Vincent Arnaud

Bibliographic record

VenueJournal of Child Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité du Québec en OutaouaisUniversité du Québec à MontréalUniversité du Québec à Chicoutimi
FundersLabEx ASLANUniversité de LyonAgence Nationale de la Recherche
KeywordsNormativePsychologyVariation (astronomy)Neuroscience of multilingualismDevelopmental psychologyLinguisticsLanguage developmentClass (philosophy)

Abstract

fetched live from OpenAlex

This study investigates four factors (age, sex, SES, and bilingualism) influencing children's language attitude (LA) development. We examine LAs in monolingual (N = 46) and bilingual (N = 71) children (59-143 months) living in France using a matched guise experiment where the children evaluated normative and non-normative variants of five linguistic constructions in French. Using a mixed-effects model, we show that children's preferences for normative variants increase with age, and each linguistic construction documented is subject to different attitudinal timeframes. The probabilities of preferring the normative variants are significantly higher for monolingual girls than for bilingual girls. Whilst lower-class and upper-class children's LAs are similar, low-to-middle-class children's responses are more random, which may illustrate the potential effects of linguistic insecurity. We discuss how the children's construction of the sociocognitive representations of linguistic variation could be explained by considering children's language exposure and experiences of socialisation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.015
GPT teacher head0.404
Teacher spread0.389 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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