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Record W4318570108 · doi:10.1002/trtr.2184

In and Out of the Unknown: Lessons from Immigrant Families Promoting Multiliteracies During the <scp>COVID</scp>‐19 Pandemic

2023· article· en· W4318570108 on OpenAlexfundaboutno aff
Guofang Li, Zhen Lin

Bibliographic record

VenueThe Reading Teacher · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGrandparentImmigrationMainstreamLiteracyFamily literacyPsychologyPandemicPedagogyDevelopmental psychologyCoronavirus disease 2019 (COVID-19)MedicinePolitical science

Abstract

fetched live from OpenAlex

Abstract How has the COVID‐19 pandemic changed the home literacy environment, parental engagement, and home‐school communications for children and families from culturally and linguistically diverse backgrounds? Data on the experiences of 231 Chinese‐Canadian immigrant families with K‐2 children revealed that emergency remote learning affected the home literacy environment in complex ways. While some experienced a decrease in mainstream language exposure, others also witnessed a decline in home language use and literacy engagement due to the closure of heritage language schools and absence of grandparents. Parents also differed in their ability, resources, and confidence level in supporting multiliteracies development at home during the pandemic. Moreover, there existed persistent barriers to effective home‐school communications despite the affordances of remote learning. The findings have important implications for both immigrant families and mainstream teachers in working collaboratively to support the children's needs in multiliteracies development.

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)0.008
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.074
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0230.007
Scholarly communication0.0060.004
Open science0.0030.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.047
GPT teacher head0.323
Teacher spread0.277 · 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 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

Citations9
Published2023
Admission routes2
Has abstractyes

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