Between Privilege and Precarity: Unpacking Language Ideologies of Chinese Students Learning Sinhalese
Bibliographic record
Abstract
Language ideologies are dynamic and sometimes contradictory across time and space. ‘Small’ languages that used to be invisible, if not devalued, have been valorized as resource to empower individual success and promote nationalist project. This study examines the language ideologies of Chinese students who used to major in Sinhalese at an elite Chinese university and who have taken up various jobs in China and Sri Lanka. In the context of China’s active engagement with South Asian countries like Sri Lanka, learning Sinhalese has been discursively conceptualized as capital to fulfil China-oriented internationalization. Data were collected from semi-structured interviews which were conducted with eight Chinese graduates majoring in Sinhalese between July 2022 and September 2022. By adopting the concept of language ideologies as a theoretical framework, this study demonstrates that learning Sinhalese opened up new spatiotemporal imaginations for Chinese students to capitalize on their performance and enact their privileged identities. However, findings also indicate that the convertibility of learning Sinhalese language was not neutral but subordinated to multiple actors including English, gender, local and transnational markets, family status and working conditions. This study contributes to the understanding of embedded nature of learning ‘small’ languages in relation to China’s socioeconomic transformation and regional integration. The study can shed lights on how power relations between language learners and structural constraints get played out in non-Anglophone countries. The study is closed by offering relevant implications for maintaining the resilience of learning languages other than English in China and beyond.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".