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Record W6944141651 · doi:10.17605/osf.io/ydqvb

Language attitudes, practices, and concerns of bi-/multilingual families raising infants and toddlers

2021· article· en· W6944141651 on OpenAlexaboutno aff

Bibliographic record

VenueOSF Preprints (OSF Preprints) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsFocus groupContext (archaeology)Raising (metalworking)Focus (optics)Language developmentQualitative researchSample (material)

Abstract

fetched live from OpenAlex

The early years are a critically important period in language acquisition. Infants’ and toddlers’ language exposure and interactions, which predominantly occur in the family context at this age, are foundational to successful development. However, in families who use two or more languages, children’s language environments and their acquisition achievements are highly variable and complex. Many parents have questions and concerns about how to best support their bi-/multilingual infants and toddlers, but unfortunately science has so far provided few empirically-supported answers. This project investigates Quebec-based parents’ attitudes and ideologies regarding childhood multilingualism, their concerns, their linguistic behaviours to support their children’s multilingual development, and their use of resources in order to do so. Our focus is on Quebec-based families across a full range of bi- and multilingual language configurations: English-French, English-Other, French-Other, and English-French- Other. In Stage 1 of the project, we conducted small-scale focus groups and interviews to identify key themes, using open-ended questions and discussions. Stage 2 now focuses on quantitative as well as qualitative data elicited from a much larger sample by means of a questionnaire (based on the focus group and interview findings).

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)0.003
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0490.007

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 head0.069
GPT teacher head0.440
Teacher spread0.370 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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