Victim Impact Assessment in India: A Legal and Policy Perspective
Bibliographic record
Abstract
Victim Impact Assessment (VIA) is a crucial yet evolving component of criminal justice systems globally, aimed at recognizing the rights and experiences of victims. In India, the emergence of victim-centric jurisprudence has reshaped how justice is conceptualized beyond retribution or deterrence, introducing rehabilitation and restorative justice as critical paradigms. VIA serves as a structured mechanism to assess the physical, emotional, psychological, and economic consequences that crime imposes on victims. While Indian law does not have a fully codified framework for Victim Impact Statements (VIS), judicial recognition—especially post the 2009 amendment introducing Section 357A into the Code of Criminal Procedure—has strengthened the victim's role in sentencing and compensation. This paper examines the conceptual basis of VIA, explores statutory provisions, key judicial pronouncements, and compensation schemes across Indian states. It further compares Indian approaches with international models such as those in the United States, Canada, and the United Kingdom to identify best practices and gaps. The paper critiques existing challenges in implementation, including bureaucratic inertia, inconsistent compensation schemes, and lack of victim participation. Recommendations include establishing uniform protocols for VIA, enhanced legal aid, training for judicial officers, and digital integration for case tracking. The study argues for a victim-sensitive justice system that balances procedural fairness for the accused while affirming the dignity, rights, and recovery of the victim. Recognizing and institutionalizing VIA is pivotal in transforming Indian criminal justice from punitive isolation to inclusive justice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".