A Review on Women Safety in India using Machine Learning on Different Social Media Platform
Bibliographic record
Abstract
Present days women's and girls are facing issues of abuse harassment, not only in society and also in social media in various forms all over the India. The safety measures and protections towards the women are very less in social media compare to real life situations. This review paper basically focuses on the review on women safety in different social media platforms across the Indian cities. The website and apps such as twitter, Facebook, Instagram and more. This paper basically focuses on women's safety in social media and to protect them in every place. Tweets on twitter, posts on face book, Instagram which contains the videos and images, any written text and quote which are abusive the women's or treat to them and less protection to women's in different areas of India can be used to understand by the youth of India and to take the strict action on them who misuse the women's safety who harass them in social medias via tweets, posts, text should take the strict action on them. tweets on twitter and the posts on Facebook and Instagram where the women share there views which spread all over the world as a stand for women or girls to explain their views, and opinions where they felt bad while when planned to go out for work and moving in a public places and transport and we can understand what actually they are feeling when they are in unknown place or harassed by unknown people and weather they are feeling safe or not.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".