GENDER DISCRIMINATION IN INDIAN LAW: A CRITICAL ANALYSIS OF LEGAL PROTECTIONS FOR MEN
Bibliographic record
Abstract
The research examines the gender inequalities inherent in the Indian legal system specifically the factor of exclusion of males through the protection of domestic violence, sexual crimes and harassment in the workplace. Although the Indian laws, like the Bharatiya Nyaya Sanhita (BNS) 2023, the Domestic Violence Act, and the POSH Act have played a key role in protecting women, they are mainly gender-oriented disregarding the female and LGBTQ+ victims. By examining the current laws, comparing international models, and applying case-study to it, this paper can see the structural bias that only sees men as attackers. It claims that the provisions of the law are one-sided and not only do not uphold the principle of equality under Article 14 of the Indian Constitution, but also promote the stigmatization of society, which causes underreporting, psychological distress, and disenfranchisement of male victims. The research is based on the global practices in the US, UK, Canada, and Australia, thus recommending the use of gender-neutral laws in India as an urgent practice. It suggests modifications in the criminal and family law, comprehensive safeguards in the workplace and domestic violence legislation, and national sensitization to eliminate the gender stereotypes. The paper concludes that true gender justice can only be achieved when laws are created to safeguard everyone regardless of gender on the nature of harm and not identity. Keywords: Gender discrimination, Men’s rights, Legal reform, Gender-neutral Laws, Domestic violence, Sexual offences, Family Law, Workplace protections.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.021 | 0.036 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".