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Record W2170126342 · doi:10.3386/w13452

How Airline Markets Work...Or Do They? Regulatory Reform in the Airline Industry

2007· report· en· W2170126342 on OpenAlexaff
Severin Borenstein, Nancy L. Rose

Bibliographic record

VenueNational Bureau of Economic Research · 2007
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsUniversity of Toronto
FundersJohn Simon Guggenheim Memorial Foundation
KeywordsWork (physics)Regulatory reformBusinessIndustrial organizationInterliningMarketingEconomicsMarket economyEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Following a brief review of the U.S. domestic airline industry under regulation (1938-1978), we study the changes that have occurred in pricing, service, and competition in the 28 years since deregulation. We then examine some of the major public policy issues facing the industry: (a) the sustainability of competition and volatility of airline profits, (b) possible market power of dominant airlines, and (c) congestion and investment shortfall in the airport and air traffic infrastructure.

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)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.006
Scholarly communication0.0090.016
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0150.003

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 head0.422
GPT teacher head0.471
Teacher spread0.049 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations148
Published2007
Admission routes1
Has abstractyes

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