RANDOM WRONGFUL CONVICTION AND EXONERATION, RARE COMPENSATION: A NEED FOR A COMPENSATION STATUTE IN BANGLADESH
Bibliographic record
Abstract
It is extremely difficult, not impossible, to determine the number of wrongful conviction in Bangladesh, mainly for the lack of initiative by the government and want of awareness among general people, advocates, rights groups, judges and others. It can undoubtedly be said that in Bangladesh many unjustly convicted are spending their lives in prison with intolerable sufferings and some of them have been released without any compensation. By analyzing the judicial decisions of the High Court Division of the Supreme Court of Bangladesh, the paper tries to highlight the frequency of wrongful conviction and exoneration in Bangladesh. This study also focuses the sufficiency of the present statute or tort law for compensating the unjustly convicted persons and highlights how better compensation can be ensured to the wrongfully convicted individuals in Bangladesh after consulting the statutes and States` practices of USA, UK, Canada, Australia, and India
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".