A critical analysis of the standard of consent in rape law in India
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".