Decentralizing Legislation in China’s Law on Legislation Amendment
Bibliographic record
Abstract
We present a novel account of China’s recent move to decentralize legislation through amending the Law on Legislation (LL). Conventional wisdom pervading both Chinese political discourse and social scientific scholarship on China portrays law as incompatible with experimentation and as only suitable for codifying policies adopted after experimentation. Moreover, the value of legislatures is viewed as lying in their independence from the executive branch. We highlight rationales offered by the Chinese Communist Party for the LL amendment that repudiate these assumptions: the Party proclaimed the intention to promote lawmaking as a central instrument of policy experimentation; moreover, the Party’s intervention in legislative processes may rescue legislatures from their irrelevance. We trace this new position regarding the role of lawmaking through the amended LL’s legislative history and initial implementation. We further show how this new official ideology clashed with the views of legislative officials, for whom “constraining government” has become a central preoccupation—both as a consequence of, and reinforcing, legislation’s political irrelevance. We argue that, to understand the political calculus underlying Xi’s approach to law, one does well to acknowledge the coherence and appeal of initiatives such as the LL amendment.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".