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Record W3160287789 · doi:10.14746/sr.2021.5.2.03

NAVIGATING THE CUMULATIVE EFFECTS OF FAMILY LANGUAGE POLICY DURING CHILDHOOD FOR IMMIGRANT YOUTH IN CANADA

2021· article· en· W3160287789 on OpenAlexaffabout
Megan MacCormac, Katherine MacCormac

Bibliographic record

VenueSociety Register · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsImmigrationIdentity (music)Everyday lifeLife course approachFamily lifeSet (abstract data type)PsychologyDevelopmental psychologySociologyGender studiesPolitical science

Abstract

fetched live from OpenAlex

One of the most influential decisions that immigrant parents must make for their children involves establishing a set of rules and norms governing what language(s) they will be raised with and how they will acquire proficiency in the dominant languages of the host society, a process known as family language policy. Such decisions can have long lasting effects for immigrant children into adulthood by influencing their integration into the host society and transition towards adult life. Using retrospective, in-depth interview data collected from young immigrant adults, this study explores the ways that parental decisions made throughout an immigrant child’s life course regarding language use and learning shape their multilingual identity and attitude towards the use of multiple languages in their everyday adult life. Findings suggest that the linguistic decisions parents make in the early years of an immigrant youths’ life have lasting impacts on them in terms of connecting to family members and culture in adulthood. We found that when parents created either a flexible or strict family language policy, such policies produced more positive experiences in the migration and early settlement process for immigrant youth compared to those whose parents did not form a family language policy.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.011
GPT teacher head0.293
Teacher spread0.283 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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