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Record W3125534413

Legal Liability for Environmental Harm from Deep Seabed Mining: Synthesis and Overview

2018· article· en· W3125534413 on OpenAlexaff
Eden Charles, Alastair Neil Craik, Tara Davenport, Hannah Lily, Ruth MacKenzie, Andrés Serra Rojas, Julia Xue

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsBalsillie School of International AffairsUniversity of Waterloo
Fundersnot available
KeywordsLiabilityHarmContext (archaeology)Strict liabilityDamagesBusinessEnvironmental planningLegal liabilityTortWork (physics)Environmental resource managementLawPolitical scienceEngineeringEconomicsEnvironmental scienceGeographyFinance
DOInot available

Abstract

fetched live from OpenAlex

A critical component of the development of international rules governing the exploitation of deep seabed minerals is ensuring that in the event of harm to the environment, persons and property, there are appropriate rules and procedures ensuring adequate and prompt compensation be paid to address those losses. The unique features of the deep seabed mining regime, including the complicated mix of state and non-state entities involved in the activities, and the status of the Area as the common heritage of mankind raise new and complicated legal issues. This paper provides an overview and synthesis of the work of the Legal Working Group on Liability for Environmental Harm from Activities in the Area (LWG), an experts group convened to identify and analyze legal issues that will need to be addressed in preparation of sector specific liability rules for deep seabed mining. In addition to providing a summary of the LWG’s papers, this paper provides an overview of the basic architecture set out in the United Nations Convention on the Law of the Sea respecting liability for harm arising from activities in the area, and the broader objectives of liability regimes in the context of environmentally risky activities. The paper identifies key issues and policy determinations that will need to be addressed as the liability rules are formulated, including questions related to the appropriate standard of liability, issues of standing, calculation of damages and the available of funds for compensation through insurance and like mechanisms.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.000

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 head0.009
GPT teacher head0.240
Teacher spread0.231 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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