Carving an Indian Mosaic for Image-Based Sexual Abuse
Bibliographic record
Abstract
In 2021, India crossed the threshold of the quantum of revenge porn cases, necessitating a public discourse on compartmentalizing the offence within pre-existing, yet outdated, legislations. Exacerbated by the COVID-19 pandemic, these reports come at the heels of the growing image-based sexual abuse in India, exemplified by other cases of voyeurism and sextortion. Despite the increasing statistics, image- based sexual abuse, as a systematic class of sexual crimes against women and children, remains virtually unrecognized in Indian scholarship and legislation. This paper proposes a comprehensive legislative framework for the battle against image-based sexual abuse. In doing so, it classifies each element of image-based sexual abuse and traces the inadequacies in the current legislative framework available for its criminalization. It creates an extensive typology for the offence, limiting and clarifying its scope. Thereafter, it under- takes an extensive survey of the solutions adopted across the globe, primarily available in the Commonwealth. In doing so, it first analyses the different foreign criminal statutes addressing Image-Based Sexual Abuse including the United Kingdom, Canada, and Australia. It also notes jurisprudence regarding the offence, and uses the experience of other countries to create a more ideal solution. Subsequently, it considers the civil solutions available to combat image-based sexual abuse and its critique. Most tortious and other solutions present in common law are addressed and explored. Post this, the paper moves towards proposing a statute to comprehensively tackle this heinous sexual offence. The resulting proposed structure contains preventive, penal, and non-penal provisions specifically adapted to suit the modern Indian legal jurisprudence.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".