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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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

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)0.0110.007

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; both teacher heads agree on what is shown here.

Study designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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