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Record W4395479504 · doi:10.1016/j.jatrs.2024.100014

Status quo and challenges in air transport management research

2024· article· en· W4395479504 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the Air Transport Research Society · 2024
Typearticle
Languageen
FieldEngineering
TopicAir Traffic Management and Optimization
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersH2020 European Research CouncilAgencia Estatal de InvestigaciónEuropean Research CouncilHorizon 2020 Framework ProgrammeNational Natural Science Foundation of ChinaEuropean Commission
KeywordsStatus quoBusinessAir transportPolitical scienceAeronauticsEngineering

Abstract

fetched live from OpenAlex

Air transport management research, concerned with all facets of aviation operations, policies, and strategies, is an essential element of making our aviation system more sustainable and preparing it for the challenges inherent to the present and future. Based on a data-driven categorization of almost 2,000 papers published on the subject, we discuss the status quo in air transport management research. Through our data-driven categorization we have identified 15 broad topics. For each topic, we provide a description of the state of the art and propose 2-3 challenges, respectively. Overall, our study provides a set of 35 challenges to the research community. Accordingly, we hope and believe that our study makes a valuable contribution, mainly by guiding the air transport management research community towards a delineated work plan on the research landscape of air transport as well as the present challenges, ultimately helping to improve the global air transport system.

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.

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)0.005
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.096
GPT teacher head0.334
Teacher spread0.238 · 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