Unpacking the Emotional Experiences of English Majors Preparing for Postgraduate Entrance Exam in China
Bibliographic record
Abstract
As a neoliberally-driven test, China’s postgraduate entrance exam is gaining an increasing attention nationwide due to the intense competition in the local market and the dramatic size of Chinese students taking the exam. Under the neoliberal mechanism, Chinese candidates are ideologically self-regulated and emotionally-driven to mobilize their resources and maximize their opportunities to pass the exam. Seeing emotion as sociopolitically loaded and ideologically driven, this study unpacks the emotional experiences of Chinese students preparing for postgraduate entrance exam. Findings indicate that, despite their successful performance, Chinese postgraduate students have experienced a series of negative emotions imposed by various socioeconomic factors. Findings also show that their emotional experiences are intertwined with their different identity construction and negotiated between social relations and power. Overall, this article highlights the importance of addressing the need of studying the emotions and language learners from the sociopolitical perspective. The study is closed by the implications for language education and language policy.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".