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Record W4408810334 · doi:10.53555/sfs.v10i1.1720

An Analytical Study of Parliamentary Privileges in India with Light On Judicial Review

2023· article· en· W4408810334 on OpenAlexvenueno aff
Shailesh Tripathi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicJudicial and Constitutional Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceLawParliamentPolitics

Abstract

fetched live from OpenAlex

The concept of parliamentary privileges refers to the legal protections that are afforded to certain members of a nation's parliamentary body. These rights grant immunity from civil and criminal crimes. These activities include words and actions taken by these lawmakers while performing legislative obligations. Article 105 of the Constitution of India provides a definition for them as well. The functions, rights, and immunities of the Houses of Parliament in India are laid out in detail in Article 105 of the Indian Constitution. This Article protects parliamentarian’s free speech. It also offers these members protection from legal actions for everything they say or vote on in Parliament or its committees. Can courts evaluate these privileges? This Article introduces the complicated concept of parliamentary privilege with a light on Judicial Review. It analyses the nature and origins of parliamentary privilege, some of the debates and tensions that have surrounding it, and how privilege is perceived and expressed in India via major texts. The law should periodically clarify these authorities, privileges, and immunities so that everyone is on the same page. These rights are regarded as exceptional provisions, and as such, they take precedence over other considerations in the event of a disagreement. The Article thus also covers in detail, the need for codification of these privileges and few important cases attached hereto

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)0.016
metaresearch head score (Gemma)0.051
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 score0.032
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.010
Science and technology studies0.0080.011
Scholarly communication0.0110.004
Open science0.0020.003
Research integrity0.0020.003
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 head0.188
GPT teacher head0.361
Teacher spread0.173 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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