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Record W4403471121 · doi:10.54097/p9qrcz57

Research on Strengthening China's Economic Losses from Ship Oil Pollution Based on Compensation Mechanisms

2024· article· en· W4403471121 on OpenAlexaboutno aff
Mei Gai

Bibliographic record

VenueHighlights in Business Economics and Management · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsChinaOil pollutionCompensation (psychology)PollutionNatural resource economicsBusinessEnvironmental scienceEnvironmental planningEnvironmental protectionEconomicsPolitical scienceLawPsychologyEcology

Abstract

fetched live from OpenAlex

The oil pollution compensation system in China was established late, and the design of the legal system is not perfect, which exposes many problems in the practice of claim settlement. This paper analyzes the present situation and problems of compensation mechanism for oil pollution damage from ships in China, and draws lessons from the legislative experience of Canada and the United States, and puts forward some suggestions for improvement. The specific contents include the legislative status and existing problems of compensation for oil pollution damage from ships in China, the relevant legislative status of representative countries, the comparison of three major international compensation systems, and the legislative choice and specific system suggestions of China. By using the method of literature analysis, this paper systematically analyzes the legislative status, advantages and disadvantages of compensation for oil pollution damage from ships in various countries through the study of relevant legal documents and practical cases at home and abroad. Through comparative study and drawing lessons from international experience, it is suggested that China adopt a dual-track compensation mechanism.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score1.000

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)0.0010.000

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 head0.020
GPT teacher head0.248
Teacher spread0.229 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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