Antagonistic Judicial Review of Bangladesh: A good candidate for the Dialogic Model?
Bibliographic record
Abstract
The Dialogic Model of judicial review famously curved out of the Canadian Charter of Rights and Freedoms, 1982, and later endorsed by the UK Human Rights Act 1998, has inspired many judicial review - strong or weak - systems worldwide. This paper argues that it has relevance for the “antagonistic” strong form judicial review system of Bangladesh as well. Building upon how the Parliament and judiciary in Bangladesh (un)relate each other, this paper argues that Dialogic Model could solve confusions in three particular areas of Bangladeshi judicial review – fundamental right based statute review, fundamental principles based collective rights review, and constitutional amendment review. It is shown that certain areas of judicial review in Bangladesh are subtly dialogic and hence could be potential breeding grounds for broader application of the Model. The Dialogic Model's own internal dilemmas and objection to its over generalisations also are noted in this paper and a case is made why those might not constitute a very big stumbling block on the way of its application in Bangladesh. This has been done through a special consideration of the comparative judicial review regimes of some of Bangladesh’s close commonwealth neighbours in south-east Asia.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".