Bibliographic record
Abstract
Abstract This article examines the performative aspect of face-to-face interactions among various legal actors and defendants in routine criminal trials in China. Using 105 trial videos as empirical data, the author develops a face-work framework to understand how an individual judge's “face”—signifying judges' legal and political roles, and their professional status—is established, protected, and enhanced during courtroom interactions. The study shows that the legal face of judges can be established by some characterizations of the nature of criminal trials such as the demarcation of legal space, the speed of the trial, and the apprising of rights to the defendants. Nevertheless, the legal face can also be disrupted by trial interactions due to judges' lack of judicial authority. Hence, Chinese judges maintain their authority through the establishment of their political face. They also use both their political face and legal face to establish their situational professional status. These interactions often lead to punitive and coercive measures against defendants in trials. While the article focuses on routine criminal trials in China, the face-work framework has the potential to explain courtroom interactions in other types of social contexts and legal proceedings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".