Evolution of Gender Neutrality in Indian Criminal Law: A Historical and Comparative Perspective
Bibliographic record
Abstract
Gender neutrality in India’s criminal justice system has emerged as a central issue in recent legal and policy debates. This reflects a gradual movement away from colonial-era, protection-focused laws toward a more equality-driven approach. For decades after independence, many criminal statutes were built on the assumption that women are the primary victims and men the typical perpetrators. This perspective left little room for recognizing the experiences of male, transgender, and non-binary survivors of crime. In recent years, however, global human rights norms and evolving moral standards have reinforced the principle that legal protection must extend equally to all gender identities. This paper examines the historical development of gender neutrality in Indian criminal law, beginning with the Indian Penal Code of 1860 and tracing its trajectory through constitutional guarantees, landmark court decisions, and major statutory reforms. These include the Criminal Law Amendments of 1983, 2013, and 2018; the Protection of Children from Sexual Offences Act of 2012; and the Transgender Persons (Protection of Rights) Act of 2019. By comparing India’s legal framework with those of countries such as the United Kingdom, Canada, and South Africa, the paper highlights practical lessons for drafting and enforcing gender-neutral laws. Although significant progress has been made, important gaps remain-most notably the lack of legal recognition for marital rape and the continued gender-specific framing of domestic violence legislation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.012 | 0.029 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".