Comparative Analysis of Rights of Victim in The Criminal Proceedings in India: Need for improving Victim Justice
Bibliographic record
Abstract
The victims of crime have long remained the forgotten identity in a judicial proceeding. Crime has been treated as wrong against society and thus the cases have been dealt as having two parties, the State and the accused. The aim of the criminal laws was focused on punishing the criminal and the plight of victims had been continuously ignored with no regard being paid to the needs or relevance of recognition of a victim as the actual sufferer of the crime. Gradually in India, the rights of victims have been recognized by the law and majorly by the courts. However, in comparison to the rights of the accused or other victim rights in other jurisdictions like UK, USA, and Canada, the Indian laws are still far behind. There is lack of specific legal provisions and uniformity in the sphere of victim rights. This study aims at in-depth analysis of the victim rights in the three stages of the criminal proceedings, i.e., investigation, enquiry and crime by simultaneously indulging in comparative analysis with rights of accused and victim rights in other jurisdiction and thereby suggesting the way forward.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".