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Record W4280583955 · doi:10.1017/s136672892200030x

The role of socioemotional wellbeing difficulties and adversity in the L2 acquisition of first-generation refugee children

2022· article· en· W4280583955 on OpenAlexafffundabout
Johanne Paradis, Adriana Soto‐Corominas, Irene Vitoroulis, Redab Al‐Janaideh, Xi Chen, Alexandra Gottardo, Jennifer M. Jenkins, Katholiki Georgiades

Bibliographic record

VenueBilingualism Language and Cognition · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsMcMaster UniversityUniversity of OttawaWilfrid Laurier UniversityInstitute for Christian StudiesUniversity of TorontoUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSocioemotional selectivity theoryRefugeePsychologySocioeconomic statusDevelopmental psychologyActive listeningNarrativeMental healthDemographyPolitical scienceSociologyPsychiatryPopulation

Abstract

fetched live from OpenAlex

Abstract First-generation refugee children often experience pre- and post-migration adversity and display high levels of mental health/wellbeing difficulties, but to date, research has not examined the impact of such factors on refugee children's L2 acquisition. Accordingly, this study examined the influence of externalizing and internalizing problem behaviours (wellbeing), time in refugee camps and low socioeconomic status (SES) (adversity) on the English-L2 abilities of 117 Syrian refugee children (7–14 years) in their third year of residency in Canada. Wellbeing difficulties and adversity factors accounted for variance on L2 vocabulary, morphosyntax, listening comprehension and narrative production tasks, beyond the variance accounted for by age of L2 acquisition and length of L2 exposure. Specifically, externalizing problem behaviours, time in refugee camp, maternal education and maternal employment predicted variance in L2 abilities. It is concluded that refugee children could have influences on their L2 acquisition that are different from those of bilinguals with other backgrounds.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.008
GPT teacher head0.268
Teacher spread0.260 · 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

Citations19
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueBilingualism Language and CognitionSame topicEducation and experiences of immigrants and refugeesFrench-language works237,207