VICTIM IMPACT STATEMENT: COMPARATIVE ANALYSIS OF THE INDIAN CRIMINAL JUSTICE SYSTEM WITH WESTERN COUNTRIES SPECIFIC REFERENCE TO USA, CANADA, UK, AND AUSTRALIA
Bibliographic record
Abstract
The Indian criminal justice system, rooted in principles of justice and equity, faces challenges in adequately addressing the needs of crime victims, often focusing primarily on punishing offenders. This contrasts with practices in Western countries, specifically the USA, Canada, Australia, and the UK, where victim-centric approaches have gained widespread recognition. Globally, there has been growing acknowledgment of the importance of giving crime survivors a voice and recognizing their rights. However, in India, victims have historically been overlooked, with limited opportunities to participate in the justice process. This study explores the legal framework surrounding victims in India and the potential for integrating Victim Impact Statements (VIS) during sentencing. The article aims to assess the efficacy of existing provisions, focusing on the judiciary's role in recognizing victims' rights and needs. By analyzing the comparative status of VIS in India, the USA, Canada, the UK, and Australia, this research identifies the strengths, limitations, and gaps in India's criminal justice system. The study also highlights the benefits of VIS in empowering victims, offering them a sense of justice, and addressing challenges in their practical implementation. Through doctrinal research, the article proposes recommendations to enhance the use of VIS in India, aiming to create a more compassionate and just criminal justice system. The article is structured in five parts: the introduction, an exploration of VIS and comparative analysis, the benefits of VIS, challenges and opportunities for victim empowerment, and concluding recommendations. This proactive approach can significantly improve how victims are treated in India, making strides towards a more victim-centric justice system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".