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Record W2311267665

Does Judicial Independence Matter? A Study of the Determinants of Administrative Litigation in an Authoritarian Regime

2015· article· en· W2311267665 on OpenAlexaff
Wei Cui

Bibliographic record

VenueeYLS (Yale Law School) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsJudicial independencePolitical scienceRule of lawPlaintiffJudicial reviewContext (archaeology)AuthoritarianismAdministrative lawLawCorporate governanceLaw and economicsDemocracyBusinessEconomicsPolitics
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.344
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2015
Admission routes1
Has abstractyes

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