Predatory Practices & Monopolization in the Airline Industry: A Case Study of Minneapolis/St. Paul
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".