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Record W2190583825

Comparing Air Transport Policies for Small Remote Communities: U.S.A., Canada, Portugal, Spain and Brazil

2013· article· en· W2190583825 on OpenAlexaboutno aff
Alda Metrass-Mendes, Richard de Neufville, Álvaro Costa, Alessando V.M. Oliveira

Bibliographic record

VenueRECERCAT (Consorci de Serveis Universitaris de Catalunya) · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
FundersFundação para a Ciência e a TecnologiaMIT Portugal
KeywordsGeographyAir transportRegional sciencePolitical scienceEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the regulatory status in the aviation industry, and the efforts of the U.S.A., Canada, Portugal, Spain and Brazil to adopt air transport policies and mechanisms to provide their populations with universal accessibility. A systems engineering grounded theory approach and a cross-national case-based comparison framework are used to look at the impacts of different policies and mechanisms on the air service to small remote communities. It is found that the success of a policy design critically depends on five factors: 1) the joint support of infrastructure investment, maintenance and operations and air services; 2) governments’ ability to promote competition and protect passengers in markets where competition does not exist; 3) the operating carrier’s choice of business model, technology for thin routes, and network; 4) political interest; and 5) local participation. Based on the evaluation of policy designs and assessment of policies in five substantially different national contexts and interviews with several stakeholders, the authors provide insights and suggest recommendations in small remote air transport policy for policy makers and practitioners. The recommendations are applicable to other countries reforming their aviation industries.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.043
GPT teacher head0.208
Teacher spread0.166 · 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.

Study designObservational
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
Published2013
Admission routes1
Has abstractyes

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