Between Shame and Pride: A Critical Ethnography of Chinese University Students Learning Lao Language
Bibliographic record
Abstract
In applied linguistics and sociolinguistics, emotions constitute an emerging space for capturing the complexity of foreign language learners’ experiences. However, our knowledge of emotion-based foreign language studies has long been dominated by English discussion, leaving a significant gap of understanding emotions of learning languages other than English (LOTE). The incommensurability in the existing literature forms a stark contrast to China’s nation-wide valorization of learning LOTE. Previous studies (Li, 2021; Li & De Costa, 2023; Li & He, 2023) show that learning LOTE is largely mediated by social, political, cultural and economic discourses across spatial-temporal contexts. Seeing emotions from a critical sociopolitical perspective (Benesch, 2017; Song, 2023), this study unpacks the emotional experiences of a cohort of Chinese students learning Lao language at a Chinese university. A longitudinal ethnography was conducted between August 2023 and May 2024 and the data included field notes, participant observation in and out of class, semi-structured interviews and written documents. Findings indicate that Chinese students learning Lao language experience both positive and negative emotions driven by sociopolitical and cultural factors circulating at macro-, meso- and micro-levels. It is argued that foreign language learners’ emotions are not fixed but dynamic and mediated in a broader process of socioeconomic transformations within and across national boundaries across time and space. The study is closed by offering some insights on conducting emotion-based foreign language studies.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".