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Record W4399848737 · doi:10.54648/aila2023017

Shaping the Future: A Comprehensive Airline Passengers ADR Framework

2023· article· en· W4399848737 on OpenAlexaboutno aff
Qiu Yizhang

Bibliographic record

VenueAir and Space Law · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAeronauticsTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Airbus and Boeing are already preparing to shape the future of air transport with their latest airliners. However, when passengers unluckily encounter disruption in their air travel, they still need to use the traditional, cost-ineffective method to seek redress for their claims, causing a psychological gap between the next-generation passenger jet and the old way of dealing with their travelling issues. The post-disruption complaint-handling system can step forward to fit the future and current demands and characteristics; a comprehensive airline passenger Alternative Dispute Resolution (ADR) framework can be a helper in shaping the future experience in aviation dispute resolution. Such a comprehensive framework could serve various types of passengers’ claims from small to major under the Montreal Convention or Regulation (EC) No. 261/ 2004, and the framework also has a quality augmentation mechanism (QAM) to enhance its quality, overcome its potential pitfalls and concerns, enable the framework to operate sustainably and promote the positive development of air passenger dispute resolution and the air transport sector. The QAM-enhanced comprehensive aviation ADR framework can, together with innovative air transport, renovate the air transport service from ‘nose to tail’.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.047
GPT teacher head0.253
Teacher spread0.206 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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