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Record W4385068601 · doi:10.6000/2817-2302.2023.02.08

Current Legal Problems in Interpreting International Civil Aviation Law

2023· article· en· W4385068601 on OpenAlexaff
Ruwantissa Abeyratne

Bibliographic record

VenueFrontiers in Law · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsMcGill University
Fundersnot available
KeywordsCivil aviationAviation lawConventionLawObligationInternational lawPolitical scienceTreatyCLARITYAviationEngineeringPublic international law

Abstract

fetched live from OpenAlex

The progress of international civil aviation law is anchored on two main sources: The Convention on International Civil Aviation (Chicago Convention); and Resolutions adopted by the Assembly of the International Civil Aviation Organization (ICAO). These two sources give rise to subsidiary guidance in the form of Annexes to the Convention and manuals on various subjects that address international civil aviation. However, there is no cohesive link between the two sources as well as there being no formal recognition by ICAO of the legal status of Assembly Resolutions, although such resolutions are adopted at each ICAO Assembly with monotonous regularity. Added to this conundrum is the lack of clarity in the interpretation of the Convention itself, which empowers the Council of ICAO to adopt Annexes to the Convention (which, according to the Convention are so named for convenience) while at the same time taking away any legal obligation of the member States to adhere to the Standards contained in the Annexes. This article discusses the nature of international civil aviation law against the backdrop of treaty law and examines the legal issues that arise from the interpretation of the Chicago Convention and Resolutions adopted by the ICAO Assembly.

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)0.069
metaresearch head score (Gemma)0.083
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 score0.069
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.011
Science and technology studies0.0130.082
Scholarly communication0.0320.028
Open science0.0080.007
Research integrity0.0170.022
Insufficient payload (model declined to judge)0.0060.003

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 head0.014
GPT teacher head0.300
Teacher spread0.286 · 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
Published2023
Admission routes1
Has abstractyes

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