MétaCan
Menu
Back to cohort
Record W2119591745 · doi:10.3828/jtep.2006.40.3.425

An Analysis of Airport Pricing and Regulation in the Presence of Competition Between Full Service Airlines and Low Cost Carriers

2006· article· en· W2119591745 on OpenAlexaff
Xiaowen Fu, Mark Lijesen, Tae Hoon Oum

Bibliographic record

VenueJournal of transport economics and policy · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLow-cost carrierCompetition (biology)Downstream (manufacturing)Service (business)BusinessIndustrial organizationFull serviceMicroeconomicsAir travelAviationTransport engineeringOperations researchEconomicsMarketingCommerceEngineering

Abstract

fetched live from OpenAlex

Despite the increasing trend of airport privatisation and deregulation in recent years, few studies have analysed how the pricing behaviour of unregulated airports affects downstream airline competition, especially the competition between airlines offering differentiated services such as the case of full service airlines (FSA) vis-à-vis low cost carriers (LCC). In this paper, a duopoly model is used to analyse and capture the differential competitive effects of changing airport user charges on FSAs and LCCs. Numerical simulations and sensitivity tests are performed in order to measure such differential effects. The analytical and numerical results found the existence of asymmetric effects of an airport's monopoly pricing on LCC and FSA. It is important for governments to take account of asymmetric effects when considering the form and extent of regulation or deregulation.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.242
Teacher spread0.223 · 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 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

Citations92
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of transport economics and policySame topicAviation Industry Analysis and TrendsFrench-language works237,207