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Record W3210711039 · doi:10.82308/54484

Implications of code-sharing agreements on air carriers' liability

2000· article· en· W3210711039 on OpenAlexaboutno aff
Audrey. Guelfi

Bibliographic record

VenueeScholarship@McGill (McGill) · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsLiabilityBusinessCode (set theory)Computer scienceProgramming languageSet (abstract data type)Accounting

Abstract

fetched live from OpenAlex

Recognised as an excellent tool for competition in the current liberalised framework of international air transport, code-sharing is becoming a common practice, as an integral part of the activity of an airline, with obvious implications for both airlines and passengers. This thesis presents two predominant legal implications of such a practice, involving two carriers for a single flight: the contracting carrier and the operating carrier. First, this study aims at examining the relationship between users/passengers and code-share partners, more particularly identifying the practice as misleading due to the non-disclosure of the actual operator of the flight, which is magnified by the inaccuracy and shortcomings of computerised reservation systems (CRS). The regulatory framework in this regard is described and the legal obligation to disclose the identity of the actual carrier is given top priority. The delimitation of operational responsibilities will also be addressed (inadmissible passengers, overbooking and baggage concerns). The private agreement between the code-share partners will be given importance in ascertaining the liability issues. Second, the current international liability regime is analysed with a view to consider the code-sharing scenario. The potential conflict between the different international legal regimes governing air carriers' liability is highlighted in order that this aspect be taken into account by the code-share partners in their contractual agreement. Last but not least, some provisions of the new Montreal Convention of 28 May 1999 will be examined. A closer look will be given specifically to those provisions of Chapter V that are particularly applicable to a code-sharing situation.

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)0.022
metaresearch head score (Gemma)0.056
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: none
Teacher disagreement score0.030
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0100.020
Scholarly communication0.0110.010
Open science0.0030.010
Research integrity0.0140.012
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.292
Teacher spread0.266 · 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

Citations1
Published2000
Admission routes1
Has abstractyes

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