Indigenous Linguistic and Cultural Practices as Mediated Resources to Fight Against the Covid-19 Pandemic: A Case of a Zhuang-Centered Border Town in China
Bibliographic record
Abstract
Previous studies on multilingual crisis communication seem to center on developed countries or cosmopolitan cities. Our knowledge about how linguistic minorities get access to health-related information in peripheral regions remains under-explored in the existing scholarship. Given that China’s border towns are peripherally located and inhabited multilingual and multicultural populations, it is of significance to understand how linguistic minorities overcome their communication barriers in times of crisis. Adopting Emergency Language Services (ELS) (Li, Rao, Zhang, & Li, 2020) as a theoretical framework, this study makes a six-month ethnographic study with Zhuang people on how they mobilize their linguistic and cultural resources to get access to health-related information during the Covid-19 pandemic. Multiple types of data were collected from six Zhuang people of diverse backgrounds in age, gender, education and language through semi-structured interviews, participant observation, field notes and online interactions. Findings demonstrate that traditional Zhuang folk arts including Zhuang Tianqin Plucked Instrument, Zhuang Folk Songs, and Zhuang Clappers constitute important resources to facilitate indigenous Zhuang people’s understanding of public health information. The study also finds that Zhuang people have actively participated in fighting against the Covid-19 pandemic together with the local government by the revitalization of Zhuang language and cultural practices. This study can shed lights on including the indigenous linguistic and cultural resources as legitimate construct to participate in crisis communication and response to local and government policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".