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Record W7135954670

Regulation of the passengers′ rights in air transport

2025· dissertation· cs· W7135954670 on OpenAlexaboutno aff
Jakub Urban

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2025
Typedissertation
Languagecs
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsAir transportScope (computer science)LiabilityContext (archaeology)AviationAviation law
DOInot available

Abstract

fetched live from OpenAlex

Regulation of PassengersÕ Rights in Air Transport Abstract This rigorous thesis addresses the regulation of passengersÕ rights in air transport, focusing on Regulation No. 261/2004. With the increasing number of individuals relying on air travel, the issue of effective legal protection for passengers has become increasingly significant. The primary objective of this thesis is to provide a comprehensive analysis of passengersÕ rights under this regulation, particularly in the context of recent case law from the CJEU. The first chapter provides a historical overview of the development of passenger rights regulation in air transport, beginning with international conventions such as the Warsaw and Montreal Conventions and tracing the transition to European legal frameworks. It also explains the system of airline liability and its impact on passengersÕ legal standing. The second chapter focuses on Regulation No. 261/2004, examining its scope and application in various scenarios. It provides a detailed analysis of situations in which passengers are entitled to compensation, including denied boarding, flight cancellations, and significant delays. A crucial aspect of this chapter is the examination of extraordinary circumstances, which can exempt air carriers from liability. The thesis extensively relies on CJEU...

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)0.003
metaresearch head score (Gemma)0.006
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 score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.006
GPT teacher head0.245
Teacher spread0.239 · 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
Published2025
Admission routes1
Has abstractyes

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