Good person, good citizen? The discourses that Chinese youth invoke to make civic and moral meaning
Bibliographic record
Abstract
Abstract In a time of transition, China is formulating principles for moral and civic responsibility and action that will serve the new goals for an expanding world power. How do young Chinese people define the ‘good person’ and ‘good citizen’, and the qualities to which they should aspire, in this changing climate? How do these mesh with the public messages and the historical traditions from which they derive? Using discourse analysis we report data from 8th and 11th grade students in Shanghai and Nantong that reveal four discourses around civic and moral responsibilities, norms and goals. Discourse analysis enables us to identify the underlying explanatory narratives that attribute causality and consequence, position people and institutions, imply judgements and values, and prescribe acceptable or expected actions. The four discourses are (1) Obeying Rules and Laws; (2) Building and Maintaining Relationships; (3) Striving towards Moral Perfection and High ‘Quality’; and (4) Loving One’s Country and Contributing to Society.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".