Bibliographic record
Abstract
Legal recognition and protection for male victims of harassment remain a not sufficiently explored and important issue within the Indian legal system. While harassment affects people regardless of gender, India’s current laws, such as the Protection of Women from Domestic Violence Act (2005) and the Sexual Harassment of Women at Workplace Act (2013), mainly focus on protecting women, resulting in a large gap in laws that include men as possible victims. This gap leaves male victims vulnerable, overlooked, and judged by society, with few ways to seek justice and support. Harassment against men takes many forms including workplace sexual harassment, domestic abuse, and casting couch exploitation but court responses in India have been uneven and limited because there are no specific laws for men. Important case studies show the difficulties male victims face, social taboos, and how the #MeToo movement’s effects have sometimes been complicated by concerns about misuse, pointing to the need for a balanced way to protect all victims while avoiding false claims. A comparison with countries like the United States, United Kingdom, Canada, and Australia shows the benefits of gender-neutral harassment laws and strong victim support systems, stressing the need for India to update its laws similarly. Challenges to change include deep-rooted social beliefs about masculinity, lack of awareness, poor support systems for male victims, and political debates about misuse of women-focused laws. This paper argues for urgent changes to create fair, gender-neutral legal protections and increase understanding in courts. It also suggests public education to remove the shame around male victimhood and the creation of support services for men. Ensuring fair legal recognition and protection for male victims is necessary to build a just society that accepts and deals with harassment as a problem for everyone. A fair and open approach will improve justice and protect the rights of all victims.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".