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Record W7165390870 · doi:10.61238/ijcl.2021.10.1.03

Proportionality and Burden of Proof: Constitutional Review in India

2021· article· W7165390870 on OpenAlexaboutno aff
Shruti Bedi

Bibliographic record

VenueIndian Journal of Constitutional Law · 2021
Typearticle
Language
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProportionality (law)Supreme courtBurden of proofDeferenceStandard of reviewConstitutional lawJudicial reviewSkepticism

Abstract

fetched live from OpenAlex

The proportionality test, after originating in German administrative law and in Canada, has received enormous success the world over in the area of constitutional rights adjudication. Burden of proof is an important aspect of any area of law, as it has a decisive effect on the outcome of cases. Under constitutional law, as a part of the proportionality principle, it impacts the protection of constitutional rights. This paper seeks to examine the role of the principle of proportionality both in doctrinal discussion and in sceptical accounts of the Supreme Court of India’s emphasis on the principle, specifically from the perspective of burden of proof. I scrutinize Justice Barak’s analysis of burden of proof as a part of the necessity stage of the proportionality test and then illustrate the divergence in court practice in India. I conclude that although the Supreme Court recognises proportionality as the new standard of review, the inconsistency in its application, specifically on burden of proof, and the attitude of deference to the State result in insufficient protection against rights violations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.075
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.075
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.004
Science and technology studies0.0060.020
Scholarly communication0.0150.007
Open science0.0020.007
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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