Assessing the Legal Framework and Socioeconomic Impacts of Compensation for Wrongfully Convicted and Imprisoned Persons in Bangladesh: Challenges and Policy Recommendations
Bibliographic record
Abstract
This study delves into the social consequences of convictions in Bangladesh, underscoring the pressing call for thorough legislative and policy changes. It critically assesses the structure and its shortcomings in offering just compensation to those wrongfully convicted, as exemplified by prominent cases like Jahalam, Abdul Jalil, Javed Ali and Sheikh Zahid. Through a research methodology involving literature reviews, case studies, interviews and surveys, the study sheds light on the psychological and financial burdens exonerees and their loved ones face. Comparative analyses of compensation mechanisms in countries like the United States, United Kingdom, Canada, and Australia reveal best practices and underscore the gaps in Bangladesh's current system. Recommendations include enacting specific compensation legislation, establishing a dedicated compensation fund, enhancing procedural safeguards, and offering comprehensive post-exoneration support. By implementing these measures, Bangladesh can better align with international human rights standards and uphold the constitutional rights of its citizens. This study aims to contribute to the broader discourse on justice reform, advocating for a structured and humane approach to addressing wrongful convictions. The findings underscore the importance of legal and institutional reforms in ensuring that justice prevails for those wrongfully convicted, ultimately reinforcing the integrity and fairness of Bangladesh's judicial system.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".