Bibliographic record
Abstract
The cases of mob lynching against the vulnerable groups are the matter of grave concern in contemporary Indian Society which is the worst form of crime against humanity. Today, people belong to vulnerable groups such as minorities and Dalits are seriously attacked and assaulted to death by mob of people of a particular community. These cases of mob lynching definitely affect the way of life and sense of well-being of minorities and Dalits to a large extent causing a fracture in their social and personal status in society which they have got as a human being. The objectives of the study are to understand the nature of mob lynching in the socio-cultural context of India and to examine the linkage between social media and mob lynching. The present study employs content analysis for the study of mob lynching. The data have been collected from lynching affected regions of the country through various news papers (Hindustan Times, The Hindu, The Indian Express and The Times of India etc., Delhi Edition) and monthly magazines (India Today and Economic Political Weekly etc.). The cases of mob lynching have been collected from March, 2013 to September, 2019. The study has found that the cases of lynching are committed against minorities and Dalits due to suspicion of beef consuming, cow slaughtering, skinning of dead cows child lifting, and theft. The study also indicates that most of cases of mob lynching are committed due to fake news, rumors and hate speeches which are circulated on social media platforms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".