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Record W2491598180 · doi:10.1075/sibil.32.14man

First language use and language behavior of Chinese students in Toronto, Canada

2006· book-chapter· en· W2491598180 on OpenAlexaboutno aff
Evelyn Yee-fun Man

Bibliographic record

VenueStudies in bilingualism · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsPsychologySociologyHistoryPhilosophy

Abstract

fetched live from OpenAlex

Many immigrant minorities are concerned with the first language (L1) maintenance of their children living in a majority language and culture. The role of the L1 has important implications for students’ linguistic, cultural and identity development. This chapter examines the relationship between Chinese students’ L1 use and behavior and the broader sociolinguistic and sociocultural environment in relation to the ethnolinguistic vitality of the Chinese community in Toronto, Canada. One hundred and fifteen Chinese students in international languages programs in Toronto participated in the study. Results indicate that the Chinese students have a positive attitude towards various factors at the sociological, socio-psychological and psychological levels which are conducive to L1 maintenance. Whether in terms of objective vitality factors, interpersonal contacts, contact with the media, heritage education support, or subjective vitality beliefs, students’ positive attitudes help facilitate their L1 use and affect their L1 behavior.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.055
GPT teacher head0.465
Teacher spread0.410 · 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 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

Citations10
Published2006
Admission routes1
Has abstractyes

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