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Record W1646381298 · doi:10.54648/aila2013010

<i>Ma Meilan v. Thai Airways International Public Company Limited</i>

2013· article· en· W1646381298 on OpenAlexaboutno aff

Bibliographic record

VenueAir and Space Law · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsDamagesJurisdictionLawConventionInternational lawInternational courtPolitical scienceBusinessLaw and economicsPublic international lawEconomics

Abstract

fetched live from OpenAlex

The Warsaw Convention and the Montreal Convention have coined different mechanisms for the calculation of damages payable by the airlines engaged in international carriage in the event of injury to passengers during the flight. Apparently, passengers who receive such international carriage services are entitled to claim for damages under specified circumstances. Nevertheless, such claim may be frustrated by various expected or unexpected obstacles and the Ma Meilan case is a typical example of such frustration. This thesis intends to explore into the diversified and intricate issues with respect to the claim made by the injured passenger against an airline conducting business of international carriage. Such issues, as perceived and surveyed by the author, may not only exclusively be relevant to the claim in the Ma Meilan case under Chinese legal regime but may likewise be encountered in other jurisdictions, which need to be classified and analysed for proper solutions. Such issues include but are not necessarily limited to the jurisdiction of the national court seizing the case, the legal basis or rationale upon which the court may render its judgment or decision, the construction by the court of the different international treaties for the purpose of their application or exclusion or even the relationship between the municipal law and international law with regard to the legal processing of the claim raised by the passenger against international airlines. All these issues are examined in this thesis perhaps without definite solutions or answers, yet it is still conducive if these issues are clearly identified and raised for further discussions with both academic and practical significance.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.078
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0090.002

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 head0.017
GPT teacher head0.259
Teacher spread0.242 · 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
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
Published2013
Admission routes1
Has abstractyes

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