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Record W3174816052 · doi:10.26552/pas.z.2021.2.21

Unruly passengers on board aircraft

2021· article· en· W3174816052 on OpenAlexaboutno aff
Mariana Lásková, Alena Novák Sedláčková

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Aviation
Canadian institutionsnot available
Fundersnot available
KeywordsJurisdictionConventionTreatyEnforcementAviationLaw enforcementOrder (exchange)LawDeterrence theoryPolitical scienceOn boardComputer securityBusinessAeronauticsEngineeringComputer science

Abstract

fetched live from OpenAlex

Unruly behaviour on board aircraft can cause a minor inconvenience to the other passengers, or else, it can escalate to such a degree where the passengers’ safety is jeopardised. Over the last three decades, the number of unruly passenger incidents has increased dramatically. The frequency and severity of such incidents had become a growing concern of the international community and aviation industry itself. Consequently, different preventive and countermeasures have been implemented to cope and deter such behaviour. The primary aim of this paper is to focus on the legal aspect of trying and prosecuting the offenders who have committed an offence or act that jeopardises the safety of aircraft or good order on board. This was accomplished by analysing the international legal framework governing unruly behaviour, namely the Tokyo Convention of 1963 and its amending Montreal Protocol from 2014. The main factor that was observed is the way how these legal instruments addressed the provisions for trying the alleged offenders and their effectiveness in the deterrence of unruly behaviour. In this paper, formal legal and case-study methods, along with comparative reasoning, were used to analyse the legal instruments. The findings showed that the Tokyo Convention had made a valuable contribution to establishing an international security legal framework. However, considerable deficiencies of this treaty have hindered the global legal uniformity and effective enforcement mechanism. Those shortcomings were to be eliminated by the Montreal Protocol. Nevertheless, the analysis revealed that, while it succeeded to eliminate the most triggering shortcoming of jurisdiction, it failed to address the lack of strong enforcement and has even constrained the powers of in-flight security officers. Regrettably, that proves to impede the achievement of the Montreal Protocol’s objectives, and it sees only a small added value. Hence, further improvements are needed to ensure that it is effective in the realities faced by modern aviation.

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)0.007
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: none
Teacher disagreement score0.041
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.019
GPT teacher head0.307
Teacher spread0.288 · 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
Published2021
Admission routes1
Has abstractyes

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