(Un)civil Society in Digital China| Uncivil Society in Digital China: Incivility, Fragmentation, and Political Stability Introduction
Bibliographic record
Abstract
Once believed to empower a range of Chinese social actors, the Internet is increasingly linked to expressions of extreme incivility that violate the etiquette and norms of interpersonal and online communication. Moving beyond definitions of civility (or incivility) based on democratic norms of deliberation and reciprocity, this article argues that civility should be reconceptualized as respect for others’ communicative rights, including the right to self-expression in pursuit of social justice. This theoretical modification affirms that civility differs from politeness and allows for contextualized and comparative studies of civility and incivility across regions and polities. In China’s authoritarian online spaces, the state tacitly encourages incivility as a divide-and-rule strategy while masking its uncivil purposes with “civil” appeals for rationality and order in a society characterized by pluralism, fragmentation, and visceral conflict. The result, as contributions to this Special Section illustrate, is a toxic uncivil society in which the space for respectful civil debate is narrowed, the influence of social groups and regime critics is diminished, and state power becomes more concentrated and resilient.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".