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Record W4307237983 · doi:10.26443/glsars.v2i1.182

Air-Rail Alliances in the Context of Liability and Environmental Protection

2022· article· en· W4307237983 on OpenAlexaff
Stefan-Michael Wedenig

Bibliographic record

VenueMcGill GLSA Research Series · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsMcGill University
Fundersnot available
KeywordsAviationBusinessAviation lawDeregulationAllianceLiabilityContext (archaeology)Civil aviationInternational tradeFinanceEngineeringEconomicsPolitical scienceMarket economyLaw

Abstract

fetched live from OpenAlex

Since the deregulation of airlines in the 1970s and 80s the aviation industry has constantly tried to find new ways to engage with the increasingly competitive aviation market by expanding their outreach through strategic partnerships and global alliances. Over the past 10 years airlines have strengthened their partnerships with railway companies to offer more convenient connections for passengers to their hubs and link remote areas to their route network. These Air-Rail Alliances have helped airlines to stay competitive in the modern aviation market. This short paper will briefly examine two legal issues pertaining to Air-Rail Alliance namely whether an airline can be held liable in case of an accident during the train leg of the journey and how Air-Rail Alliances help airlines to meet their carbon emission reduction goals under the European Union Emissions Trading Scheme (EU ETS) and the ICAO Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). Especially during the COVID-19 pandemic, Air-Rail Alliances proved to be a good vehicle for airlines to replace specific flights in their network. The paper will first explain basic terminology relating to the airline’s business before explaining the structure of Air-Rail Alliances in more detail. Afterwards, it will address the question as to whether an airline can be held liable in the case of an accident during the train leg of the journey. The paper will answer this question by arguing that airlines cannot be held liable under international aviation law but rather the train operator under the lex loci of the state in which the accident occurred. Finally, the paper will discuss the structure of both the EU ETS and CORSIA and argue that Air-Rail Alliances are a valuable tool for airlines to meet their CO2 reduction goals. It will be highlighted that both regimes are flexible and adaptive enough to take the unprecedented consequences of the COVID-19 pandemic into account and underline how the law can be adaptive in such a changing environment.

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)0.007
metaresearch head score (Gemma)0.009
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 score0.012
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0090.026
Scholarly communication0.0120.014
Open science0.0020.010
Research integrity0.0110.009
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 head0.070
GPT teacher head0.353
Teacher spread0.283 · 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
Published2022
Admission routes1
Has abstractyes

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