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Record W4409679102 · doi:10.31234/osf.io/er7dk_v2

The attitudes at the heart of multilingual family language policies

2025· preprint· en· W4409679102 on OpenAlexfundaboutno aff
Ruth Kircher, Melanie Brouillard, Alexa Ahooja, Erin Quirk, Susan Ballinger, Linda Polka, Krista Byers‐Heinlein

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Institutes of HealthConcordia UniversityCentre for Research on Brain, Language and Music
KeywordsLinguisticsLanguage policyPolitical sciencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

This study focuses on parental language beliefs, which we approach from an attitudes perspective. Language attitudes are at the heart of multilingual family language policies because they shape parents’ language practices and language management – and thereby, ultimately, children’s multilingual development. Based on French and English corpora (over 30,000 words in total) comprising 742 Quebec-based parents’ responses to an open-ended survey question, we present a corpus-assisted discourse study of parental attitudes towards childhood multilingualism. We analyse frequencies and collocations to attest statistically significant patterns, and we examine concordance lines and longer text segments to establish meaning in context. Our findings confirm the multidimensionality of parental attitudes towards childhood multilingualism, providing new insights into the nature of the three previously-established dimensions – status, solidarity, and cognitive development – as well as revealing a potential fourth dimension: personality development. The study thus makes a contribution to the family language policy framework and to language attitude theory more generally. Moreover, the findings show systematic differences between parents transmitting multiple societal languages versus parents transmitting heritage languages alongside one/more societal languages. The study thus also makes a practical contribution by facilitating the development of tailored support measures for different types of multilingual families.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.282
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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Same topicSecond Language Learning and TeachingFrench-language works237,207