Emergency Language Services in a Zhuang Village in the China-and-Vietnam Borderland
Bibliographic record
Abstract
As a localized sociolinguistic concept, emergency language services (ELS) have gained an increasing importance during the Covid-19 pandemic in China. Despite the nation-wide promotion of ELS, previous studies seem to center on the language practices in the cosmopolitan cities whereas our knowledge about the peripheral regions remains poorly understood. Given that China has the largest number of bordering countries, it is of significance to conduct ELS in the borderlands. Adopting ELS (Li, Rao, Zhang, & Li, 2020) as a theoretical framework, this study investigates what ELS have been available to a Zhuang-centered minority village in Yunnan bordering Vietnam and how local people respond to the Covid-19 related messages. Based on the semi-structured interviews with two village chiefs, one rural Zhuang doctor and six Zhuang people of different ages and language backgrounds, the study finds that there are insufficient language services available to Zhuang people who are lack of proficiency in Putonghua. The grassroots efforts yet play critical roles, including rural Zhuang doctor who provides emotional support and medical treatment, and village chiefs working as language broker translating Putonghua-mediated messages into Zhuang oral language through the multiple social media. The findings and results of the study can shed lights on providing effective language services for Chinese multilingual population from peripheral regions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".