Does Judicial Independence Matter? A Study of the Determinants of Administrative Litigation in an Authoritarian Regime
Bibliographic record
Abstract
Lawsuits against the government form a part of the regular functioning of legal systems in democratic countries, and responding to such lawsuits an unavoidable part of governance. However, in the context of authoritarian regimes, administrative litigation has been viewed as a distinctively valuable institution for promoting the rule of law and individual rights. Moreover, the judiciary is portrayed as the keystone to this institution and to the rule of law in general: the more powerful and competent is the judiciary, the more it is able to “constrain government” through judicial review. Through empirical and comparative analyses of over two decades of administrative litigation in China against one of the state’s essential branches, tax collection, I challenge the utility of this normative conception of administrative litigation. Using conceptual tools that apply across legal systems and regulatory areas, I show that while litigant behavior as well the regulatory environment offer useful explanations of litigation patterns such as case volume and the plaintiff win rate, the relevance of judicial quality is barely discernible. This highlights the intuitive idea that judicial review can operate only when private parties bring suit, and whether and when they will do so cannot be taken for granted. In countries with weak legal systems, the rule of law may fail in certain basic ways that even a competent and well-subsidized judiciary cannot remedy.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".