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Record W4319844237 · doi:10.1177/0067205x221146335

Active After Sunset<scp>:</scp> The Politics of Judicial Retirements in India

2023· article· en· W4319844237 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFederal Law Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsAdjudicationPoliticsInstitutionGovernment (linguistics)Language changeSupreme courtLawPolitical sciencePublic administrationSociology

Abstract

fetched live from OpenAlex

Abstract Indian judges retire, but not into inactivity. Many pursue careers in government-appointed roles. Scaffolded around the concept of institutional corruption, this article interrogates the history, law and politics of the retirement careers of judges in India. Three questions take centre stage in this analysis: What types of careers do retired judges pursue? Why do they pursue them? How do judges’ post-retirement ambitions impact their pre-retirement decisions? The cumulative analysis suggests that the Supreme Court of India, not specific judges, benches or decisions, is institutionally corrupt. The system of post-retirement jobs cycles like an economy of influence that is weakening the institution’s effectiveness, especially its capacity for impartial adjudication in matters that involve governments. But the Indian court’s performance and its public reception also reveal unique attributes that can enrich our general understanding of institutional corruption and separate the concept’s essential features from its auxiliary ones.

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.

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)0.001
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 score0.921
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.039
GPT teacher head0.330
Teacher spread0.291 · 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