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Record W7131653110 · doi:10.3828/jtep.2022.56.2.156

Airport-airline Relationship, Competition and Welfare in a Multi-airport System: The Case of New Beijing Daxing Airport

2022· article· en· W7131653110 on OpenAlexaff
Meng Hou, Kun Wang, Hangjun Yang, Anming Zhang

Bibliographic record

VenueJournal of transport economics and policy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Competition (biology)BeijingWelfareRegulatorTariff

Abstract

fetched live from OpenAlex

A game-theoretical model is developed in the context of the regulated multi-airport system (MAS), examining the regulator's policy in airport slot allocation and airlines' airport entry decisions. The regulator has a mixing objective to put different weights on passenger surplus and airports/airlines' profits. It can choose to intervene in airline allocation (that is, one-airport-one-airline rule) or not. It is found when the regulator does not intervene in airline allocation, airlines choose to enter both airports bringing about higher welfare. However, the regulator would intervene if it puts a sufficiently high weight on airports/airlines' profits.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.349
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.050
GPT teacher head0.257
Teacher spread0.207 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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