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Record W1486234613

Predatory Practices & Monopolization in the Airline Industry: A Case Study of Minneapolis/St. Paul

2002· article· en· W1486234613 on OpenAlexaff
Paul Stephen Dempsey

Bibliographic record

VenueSSRN Electronic Journal · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsMcGill University
Fundersnot available
KeywordsMonopolizationMemphisMonopolyCompetition (biology)AviationBusinessLow-cost carrierService (business)Predatory pricingEconomicsMarket economyMarketingEngineering
DOInot available

Abstract

fetched live from OpenAlex

The monopolization of air transportation is among the most pernicious of commercial events, for the price of air transport impacts the cost of doing business in entire geographic regions. At cities like Minneapolis and St. Paul, Detroit and Memphis, the suppression of competition results in a regressive wealth transfer from consumers to producers to the tune of hundreds of millions of dollars per year. It is, in effect, a hidden tax on all who must pass through the airport. Because aviation is part of the infrastructure upon which all other businesses in a community depend, excessively high air fares dampen economic activity in whole geographic regions.For more than a decade, Northwest Airlines has been among the most aggressive carriers in responding to new entrants that dare to inaugurate service on its monopoly spokes radiating from its Fortress Hubs at Minneapolis/St. Paul, Detroit, and Memphis. Numerous studies have revealed that where there are few or no low-fare carriers disciplining an incumbent monopolist, hub premiums are high and continue to increase over time. Conversely, the greater the presence of a low-fare carrier at the hub, the lower the hub premium.Airports are public resources, paid for by taxpayers. To allow their monopolization, and the consumer exploitation which results from this, is antithetical to the public interest.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.064
GPT teacher head0.280
Teacher spread0.216 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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