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Record W4408820251 · doi:10.58837/chula.the.2023.504

Strengthening China's response to economic losses caused byship oil pollution: advancing compensation mechanisms

2023· dissertation· en· W4408820251 on OpenAlexaboutno aff
Mei Gai

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsnot available
Fundersnot available
KeywordsChinaOil pollutionCompensation (psychology)HarmLegislatureBusinessOil spillEnvironmental planningPollutionMechanism (biology)Human settlementNatural resource economicsEnvironmental protectionEnvironmental resource managementPolitical scienceEngineeringEnvironmental scienceEconomicsLawEcologyWaste management

Abstract

fetched live from OpenAlex

China's oil pollution compensation system, established relatively late, has an imperfect legal framework, leading to numerous issues in claim settlements. Frequent oil pollution incidents significantly damage its marine ecosystem and harm affected parties. Despite initial efforts, China's compensation framework is less effective than those of developed countries. This paper examines the current challenges in China's compensation mechanism for ship oil pollution damage, drawing lessons from Canada and the United States, and proposing improvements. Through a comprehensive literature review and comparative analysis, the study identifies legislative strengths and weaknesses, aiming to improve China's system by adopting a dual-track compensation mechanism that integrates international conventions with domestic laws to enhance marine environmental protection and ensure fair compensation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2023
Admission routes1
Has abstractyes

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