Political Liberalism and Political Embeddedness: Understanding Politics in the Work of Chinese Criminal Defense Lawyers
Bibliographic record
Abstract
This article examines the meanings of politics in everyday legal practice using the case of Chinese criminal defense lawyers. Based on 194 in-depth interviews with criminal defense lawyers and other informants in 22 cities across China, we argue that lawyers’ everyday politics have two faces: on the one hand, lawyers potentially can challenge state power, protect citizen rights, and pursue proceduralism in their daily work; on the other hand, they often have to rely on political connections with state agencies to protect themselves and to solve problems in their legal practice. The double meanings of politics—namely, political liberalism and political embeddedness—explain the complex motivations and coping tactics that are frequently found in Chinese lawyers’ everyday work. Our data show that the Chinese criminal defense bar is differentiated along these two meanings of politics into five clusters of lawyers: progressive elites, pragmatic brokers, notable activists, grassroots activists, and routine practitioners. They also suggest that a principal manifestation of political lawyering is not merely short-term mobilization or revolutionary struggle against arbitrary state power, but also an incremental everyday process that often involves sophisticated tactics to manage interests that often conflict.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".