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Record W3173064159 · doi:10.4236/ojml.2021.113029

Grandparents in Minority Language Maintenance: Mandarin Chinese in Canada

2021· article· en· W3173064159 on OpenAlexaffabout
Qin Xiang, Veronika Makarova

Bibliographic record

VenueOpen Journal of Modern Linguistics · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMandarin ChinesePinyinGrandparentPsychologyLinguisticsMean length of utteranceNarrativeStandard ChineseChinaFirst languageChinese charactersDevelopmental psychologyHistoryLanguage development

Abstract

fetched live from OpenAlex

This article investigates the role of grandparents in Mandarin Chinese retention by children in Saskatchewan, Canada. The materials for the study come from interviews with 60 Mandarin Chinese/English bi-multilingual children between the ages of 6 and 13 in the families of immigrants from China residing in Saskatchewan, Canada. Additional materials describing bilingual children’s Mandarin language proficiency come from a picture description as well as Chinese characters reading and writing tasks. The interview responses and language proficiency parameters were analyzed with the help of correlation analysis, chi-square (for nonparametric data) and univariate ANOVAs (for parametric data). The results are highly controversial, as on the one hand, they demonstrate that the presence of grandparents residing in the same household as their grandchildren is associated with the grandchildren having fewer non-canonical forms (NCFs referred to in earlier research as errors) in their narratives and fewer phonological NCFs. On the other hand, children who have grandparents residing with them are more likely to have a lower ability to read Mandarin texts with PinYin, spend fewer hours reading in Mandarin, produce more incomplete sentences per utterance, and shorter narratives.

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)0.007
Version: codex-gemma-dda1882f352aValidation 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 score0.302
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
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 head0.042
GPT teacher head0.435
Teacher spread0.393 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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