Obscenity and the Depiction of Women in Pornography: Revisiting the Kamlesh Vaswani Petition
Bibliographic record
Abstract
Growing instances of sexual violence against women in India are often attributed to a culture that has normalized such violence and the objectification of women. In this context, Kamlesh Vaswani’s 2013 petition before the Supreme Court of India argued that an absolute ban on pornography was necessary to attack this culture and curb sexual violence. While couched in moral outrage and calling for an unreasonably broad absolute ban on all pornography, the petition highlights some legitimate harms caused to women in India by the widespread consumption of pornography. This paper proposes a framework to address these harms within the contours of the Indian Constitution. In doing so, it will use Catharine Mackinnon’s work to draw comparisons between the harms identified by the petition and those identified by the Canadian Supreme Court in its judgment in R v. Butler. It will then argue that a shift in India’s approach to obscenity from an American-style offense to community standards approach to a Canadian-style objective harms approach is both possible under the Indian Constitutional scheme and would address these harms without creating an unreasonable restraint on free speech.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.046 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".