Motivation of Mandarin speakers learning Cantonese in a transnational context : multilingualism and investment
Bibliographic record
Abstract
Globalization has intensified the cross-border mobility of people and languages, which contributes to the number of transnationals and the prevalence of multilingualism. Mandarin and Cantonese are two Chinese varieties and the competition between them has been transported from primarily Chinese-speaking regions to regions outside of Greater China, thereby creating an international issue among these transnational language users. In some overseas Chinese communities, Cantonese used to be the lingua franca but is currently declining and being replaced by Mandarin due to political, economic, cultural, and demographic factors. However, there is a new generation of Mandarin speakers interested in learning Cantonese in some English-dominant locations, such as Vancouver, Canada. In general, Mandarin is the sole official language in Mainland China and Cantonese is defined as a dialect, but this positioning is problematized in different contexts. This thesis will focus on one transnational context to explore the motivation of official-language (Mandarin) speakers to learn a value-declining regional language (Cantonese) in English-dominant Canada. The construct of motivation has been carefully scrutinized in the field of second language acquisition (SLA) in the past decades, but I find there is a huge gap between the research on motivation to learn English and languages other than English (LOTEs). This thesis examines if the English monopoly on motivational theories can be generalized to explain Cantonese learning behaviors among a transnational group of students in a transnational context. Through examining the Cantonese learning process of 61 Mandarin speakers in a Canadian university in both quantitative and qualitative ways, the study reveals that idealized multilingual identities play a significant role in motivating students to pick up Cantonese. Integrative motivation alone fails to capture the motivational features of the Cantonese learners in this study but instrumental motivation together with other intrinsic and extrinsic factors are still functional. Based on this study, instructors should recognize students’ transnational practices and global identities as resources to develop their multi-competence. Another prominent recommendation concerns the availability of formal language education in languages such as Cantonese, which supports the expansion of Cantonese language provision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".