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A critical analysis of the standard of consent in rape law in India

2023· article· es· W4361288120 on OpenAlexaboutno aff
Nikunj Kulshreshtha

Bibliographic record

VenueOñati Socio-legal Series · 2023
Typearticle
Languagees
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsJurisprudenceLawInformed consentInterpretation (philosophy)Political scienceMedicineAlternative medicineComputer science

Abstract

fetched live from OpenAlex

This article critically analyses the standard of consent in rape law in India by engaging in a doctrinal analysis of the current jurisprudence. It also analyses various extraneous circumstances influencing the determination of consent in Indian rape law. The article will then assess the status of consent jurisprudence and related issues in the English and Canadian jurisdictions for a comparative assessment. Thereafter, it will critically assess the arguments in favour of an affirmative standard of consent before concluding with possible solutions for better interpretation of the law in determining consent by courts during rape trials in India. En este artículo se analiza críticamente el estándar de consentimiento en la ley india sobre violación mediante un análisis doctrinal de la jurisprudencia actual. También se analizan diversas circunstancias ajenas que influyen en la determinación del consentimiento en la legislación india sobre violación. A continuación, el artículo evaluará la situación de la jurisprudencia sobre el consentimiento y las cuestiones conexas en las jurisdicciones inglesa y canadiense para realizar una evaluación comparativa. A continuación, se evaluarán críticamente los argumentos a favor de una norma afirmativa del consentimiento antes de concluir con posibles soluciones para una mejor interpretación de la ley a la hora de determinar el consentimiento por parte de los tribunales durante los juicios por violación en la India.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.004
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 head0.018
GPT teacher head0.323
Teacher spread0.305 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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