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Record W4399459748 · doi:10.33002/jelp040103

A Critical Analysis of Legal Regime of Ship-Breaking and Ship-Recycling in Bangladesh

2024· article· en· W4399459748 on OpenAlexvenueno aff
Sakhawat Sajjat Sejan, Mahir Ibne Mahfuz, Umme Sayma Akter Urmi

Bibliographic record

VenueJournal of Environmental Law & Policy · 2024
Typearticle
Languageen
FieldEngineering
TopicMarine and Offshore Engineering Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementCorporate governanceStatuteBusinessEnvironmental degradationEnvironmental planningEnvironmental lawEngineeringPolitical scienceLawFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.245
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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