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Record W4246612561 · doi:10.3138/utlj.59.3.405

JUDICIAL POLITICS IN AUTHORITARIAN REGIMES

2009· article· en· W4246612561 on OpenAlexvenueno aff
R. Balasubramaniam

Bibliographic record

VenueUniversity of Toronto Law Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAuthoritarianismDialecticRule of lawLawConstraint (computer-aided design)PoliticsPolitical sciencePower (physics)Law and economicsJudicial independenceJudicial reviewAdjudicationIndependence (probability theory)SociologyDemocracyEpistemologyMathematicsPhilosophy

Abstract

fetched live from OpenAlex

This review article draws out some of the major jurisprudential lessons that can be learned from a series of case studies of judicial politics in authoritarian regimes. Such regimes need to portray themselves as respectful of the rule of law to prolong their grip on power; they therefore tolerate independent courts, because an independent judiciary is emblematic of a commitment to the rule of law. However, the regime must contend with an unintended side effect of independent courts: judges may use their independence to check the regime, limiting its power. Authoritarian regimes will thus employ strategies to contain judicial power, producing a dialectic of empowerment and constraint with respect to courts. Among the lessons highlighted is that attempts by authoritarian regimes to contain courts strain formal rule of law conditions (conditions requiring, inter alia, that laws comprise rules that are clear, non-contradictory, stable, and generally prospective, and that official action match declared rule), suggesting that the formal conception of the rule of law imposes substantive limits on arbitrary power; and that the dialectic of empowerment and constraint exhibits a problem of domination with respect to courts, as part of a larger problem of domination of legal subjects.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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 designTheoretical or conceptual
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

Citations6
Published2009
Admission routes1
Has abstractyes

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