Judicial Interpretation of China's Supreme People's Court as “Secondary Law” with Special Reference to Criminal Law
Bibliographic record
Abstract
The analysis of the profile and role of China's Supreme People's Court needs updating. The Court is actively developing new interpretative formats that concern its relations with sister organizations and the National People's Congress. This article contextualizes these formats within China's changing institutional dynamics. China does not have a separation of powers; however, the Chinese system of justice does have its own separation of functions. The Court is playing a pivotal role from within the changing separation of functions, but the extent and quality of its independence from other organizations are open to question. In the context of deepening legal reform, the law is still incomplete and imperfect, and Court interpretation has often served as “secondary law.” In short, pragmatic judicial interpretations have sometimes preceded legislation by Congress. Remedy such as secondary law might be justified as absolute administrative necessity given the outstanding structural problems that characterize China's criminal justice system, but it has attracted internal criticism that argues for narrowing the function of the Court to a more tightly disciplined judicial role as well as for plugging the holes in legal process and structure by creating guiding case law and supporting the “freedom of judge's decision making.”
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".