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Record W4408669054 · doi:10.1080/01434632.2025.2480731

The role of L2 WTC and accommodative encounters with locals in Mainland Chinese students’ sociocultural adaptation to Hong Kong

2025· article· en· W4408669054 on OpenAlexaff
Xiaoyan Ivy Wu, Stefano Occhipinti, Bernadette Watson, Kimberly A. Noels

Bibliographic record

VenueJournal of Multilingual and Multicultural Development · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSociocultural evolutionMainlandAdaptation (eye)PsychologyMainland ChinaSociologyGeographyAnthropologyChina

Abstract

fetched live from OpenAlex

It remains unclear what role language use and communication with locals play in Mainland Chinese students’ (MCSs’) sociocultural adaptation to Hong Kong. To address this gap, the present study took a language and social psychology approach by invoking Communication Accommodation Theory (CAT) and Willingness to Communicate in a Second Language (L2 WTC). Survey data were collected from 372 MCSs. A path analysis model delineated the relationships between MCSs’ Cantonese confidence, Cantonese WTC, accommodative encounters and contact with locals, and their sociocultural adaptation. The follow-up multiple regression analysis examined the paths between accommodative encounters and the variables they directly predicted (i.e. Cantonese confidence and quality of contact). The results revealed that among the CAT strategies of interpretability, discourse management, interpersonal control, and emotional expression, emotional expression carries the most weight in predicting Cantonese confidence and quality of contact. The findings offer fresh theoretical insights and valuable practical implications.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.011
GPT teacher head0.277
Teacher spread0.266 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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