Shenpi (Licensing) Reform from the Perspective of One Municipal Jurisdiction: Ideologies, Institutions, and Law
Bibliographic record
Abstract
The Administrative Licensing Law (xingzheng xuke fa) will constitute a major addition to Chinese administrative law, and the rhetoric surrounding its drafting promises path-breaking reform of the Chinese regulatory state. This report critically assesses that promise by chronicling the actual course of shenpi (licensing) reform carried out in Shenzhen in 2001. Shenpi reform derives its novelty from questioning the rationale of regulatory policies and not just the procedures by which they are carried out. In Shenzhen, however, the reform revolved around an effort to achieve quantitative reduction in the number of shenpi procedures, which could reflect either changes in the substance of policies or mere success in cutting red tape. Close examination reveals that Shenzhen's reform was a combination of house cleaning against errant rule-making and an attempt to further increase bureaucratic efficiency, whereas little was accomplished in policy reorientation. A key difficulty in overhauling regulatory policies in China is the extremely insular policymaking process, where policy development falls entirely into the hands of specialised agencies. Not only is legislative and judicial oversight over agency rule-making absent, Shenzhen's experience also suggests that accountability has been difficult to establish even within the executive branch. This is due both to the weakness of internal monitoring institutions and the limited concept of accountability the government employs. The report concludes that incremental reform is possible to allow greater input into the policymaking process and to impose greater accountability on that process, even if robust legislative and judicial supervision is not politically or institutionally feasible in the near future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".