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Record W7125480539 · doi:10.30560/les.v1n2p84

Adjustment and Integration of International and Domestic Rules on Compensation for Damage to Checked Luggage of Air Passengers—Focusing on the Montreal Convention and China's Civil Aviation Law

2025· article· W7125480539 on OpenAlexaboutno aff
Mao Weixin

Bibliographic record

VenueLaw Economics and Society · 2025
Typearticle
Language
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsAviation lawCivil aviationConventionScope (computer science)Compensation (psychology)AviationBridge (graph theory)

Abstract

fetched live from OpenAlex

In 2024, the Montreal Convention raised the compensation limit for checked luggage to 1,519 Special Drawing Rights (SDRs). This revision further highlights the lag and shortcomings of China's Civil Aviation Law and its supporting regulations in aligning with international conventions. To address the challenges of integrating international and domestic rules on compensation for damage to checked luggage, this study conducts a comparative analysis of the core provisions of the Montreal Convention and China's Civil Aviation Law, supplemented by an empirical analysis of the case Lou Mengjie v. Aeroflot Russian Airlines. The research identifies key differences between the two frameworks in terms of compensation limits and scope of application, while also revealing practical integration difficulties, such as the chaotic application of mixed transportation rules, the disconnect between the domestic compensation limit of 100 yuan per kilogram and actual losses, and deviations in the judicial application of the convention. Grounded in the theory of transformative domestication, the study proposes a three-dimensional optimization approach: unifying core convention systems through legislation, standardizing the application of laws in judicial practice, and establishing a dynamic regulatory mechanism through administrative measures. This provides actionable solutions for aligning China's aviation laws with international rules, ultimately achieving the dual goals of protecting passenger rights and promoting the sustainable development of the aviation industry.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.414

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0020.006
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
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 head0.012
GPT teacher head0.268
Teacher spread0.256 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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