Chinese students speak about their favourite teachers and university reform
Bibliographic record
Abstract
China is trying to develop better outcomes for students, build world-class institutions and ascend global university rankings. Beijing also wants to jettison the notion it is the workshop of the world and embrace a culture of innovation. Peter Jarvis thinks the world would be a better place if ordinary people were given convivial spaces wherein they can learn and play together. But discourse constructing Chinese universities hardly ever concerns day-to-day life as experienced by students and conviviality is not a priority. This study was a modest attempt to rectify this problem. Students at Chinese universities were asked about their favourite teachers and ideas for university reform. Data were collected in Yangtze Delta universities and in the Beijing region. Students mostly recall their favourite teacher as warm and kind-hearted. Regarding university reform, they want fundamental structural adjustment, democratisation, internationalisation and better integration with society. Chinese government efforts to develop universities are almost entirely located in functionalist notions of globalisation (with ‘socialist characteristics’). In contrast, student preoccupations are nested in humanist and radical humanist paradigms. Tsinghua and Peking universities are now in the ‘top-100’ on Shanghai Jiatong global rankings. However, there is a political chill in China and not much chance of student preoccupations being heard.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".