A Critical Analysis of Legal Regime of Ship-Breaking and Ship-Recycling in Bangladesh
Bibliographic record
Abstract
Shipbreaking and ship recycling is one of the major industries of Bangladesh. It is a significant contributor to Bangladesh’s economy and employment as well as one of the major sources of steel supply for the nation. However, this comes at a huge cost to the environment and human health and the current governance of the industry is unable to address this issue properly. This paper offers a comprehensive analysis of the legal framework governing ship-breaking and ship-recycling in Bangladesh, with a primary focus on environmental degradation. It critically examines both national and international instruments aimed at regulating this industry. Drawing upon extensive research and literature review, the paper evaluates the effectiveness of existing legal mechanisms in addressing environmental concerns associated with ship-breaking activities. Special attention is paid to the compliance and enforcement mechanisms within these legal frameworks. Through a comparative analysis of relevant statutes, conventions, and case law, the paper highlights gaps and shortcomings in current regulations which need to be addressed to strengthen environmental protections in the ship-breaking industry. This study seeks to augment the existing dialogues on encouraging sustainable practices in the ship-breaking and ship-recycling industry in Bangladesh by offering a nuanced understanding of the complex interactions between legal regulations and environmental degradation.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".