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Record W7011476751

Mandarin Chinese Heritage Language Maintenance among Mandarin-English Bi/multilingual Children in Saskatchewan

2021· dissertation· en· W7011476751 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsMandarin ChineseHeritage languagePrestigeFirst languageLanguage proficiencyLanguage shiftLanguage assessmentSociolinguisticsLanguage transfer
DOInot available

Abstract

fetched live from OpenAlex

So far, there have been no significant studies in Canada or Saskatchewan that examine sociolinguistic factors (such as language attitude, language use, and language exposure) as well as a factor of age vis-à-vis heritage (Mandarin) language proficiency among Mandarin-English bi/multilingual children. Mandarin has prestige in China as the language of education and government, and the number of Mandarin speakers in Canada is increasing. However, more people in Saskatchewan speak Cantonese and Chinese dialects other than Mandarin. Thus, this study examines the Mandarin language proficiency of the bi/multilingual children from Chinese-speaking immigrant families in Saskatchewan, a province with a small demographic group of Mandarin language speakers and very little support for its maintenance as compared with other provinces, such as British Columbia and Ontario. In addition, this study explores what sociolinguistic factors contribute to Chinese immigrant children’s language proficiency in these settings. The relationship between language proficiency and sociolinguistic factors was investigated via the framework of Variationist Sociolinguistics. An audio-recorded narrative task was adopted to assess bi/multilingual (Saskatchewan) and monolingual (in China) children’s oral Mandarin language proficiency. Objective linguistic proficiency parameters (vocabulary size, syntactic complexity, and fluency) were extracted from the sound records and compared bi/multilingual and monolingual children. Questionnaires and interviews were conducted to assess parents’ and children’s language attitudes and language use, and the children’s language exposure in the home and social domains. Finally, statistical relationships were performed between contextual sociolinguistic factors and language proficiency parameters. This study has shown that bi/multilingual children are overall successful in learning and maintaining Mandarin as a heritage language in Saskatchewan. While some of the critical results suggest that attending community-run Chinese heritage language schools plays an essential role in learning Mandarin, the most crucial indicator of Mandarin heritage language acquisition and maintenance is the positive attitudes of the parents towards the Mandarin as a heritage language. Of equal (if not greater) importance are their efforts to create a supportive and consistent home language environment, and to provide sufficient and varied (in terms of quality and quantity) Mandarin language input within the home and family. Since the Mandarin language is core to Chinese culture, this research offers recommendations to the Ministry of Education, Public School Boards, and the University of Saskatchewan and Regina to promote Mandarin as a foreign language and as a heritage language. This would contribute to sound bilingualism among Mandarin heritage speakers and facilitate heritage language learning, acquisition, maintenance, and 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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.257
Teacher spread0.250 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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