Cyberspace and Women- Dimensions of Cybercrime against Women in India
Bibliographic record
Abstract
The twenty-first century has been an era of inventions. Inventions that have greatly improved the quality of human life. Artificial intelligence's genesis and dominance have been witnessed. We have already entered the 5G era, which began with limited internet access. An alternate reality has emerged as a result of this unstoppable rise. An ethereal reality that promotes complete anonymity. With all of the benefits it provides, it has also proven to be lethal. With the rise of the online world came stalkers, hackers, scammers, and a slew of other miscreants and lawbreakers. As a result, society has become exposed to cybercrime. The researchers will focus on cybercrime perpetrated against women in this study. Women are easy prey for cybercriminals, and they are disproportionately victimized. Cyberbullying, voyeurism, sextortion, and stalking are all common online crimes against women. Women's privacy and security are in jeopardy as a result of the rise in cybercrime. The research's main goal is to examine the current state of cyber security in India and the need to enact specific legislation to protect women. The researchers would show how the laws are not being implemented throughout this study. The most important finding of this study is that more precise regulations and legislation against cybercrime are required. With the rise of social media networks and private websites, it is more important than ever for the government to enact special legislation for each type of crime perpetrated against women. Throughout the course of this study, an analysis of how many crimes is not reported due to traditional society and patriarchal attitudes will be offered. Researchers have also looked into the government's success in combating cybercrime and have come up with some useful suggestions for combating this threat. The researchers used a doctrinal research method and cited their sources using the bluebook method.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".