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Record W2953505723 · doi:10.1177/0361198119843861

Sustainability without Subsidy: Public Case for Vertically Integrated Rail Oligopolies for Freight

2019· article· en· W2953505723 on OpenAlexaboutno aff
John G. Allen, Gregory L. Newmark

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
FundersBulgarian National Science Fund
KeywordsSubsidyOligopolyGovernment (linguistics)BusinessSustainabilityPublic policyInternational tradeEconomicsIndustrial organizationMarket economyEconomic growth

Abstract

fetched live from OpenAlex

Maintaining rail freight networks without subsidy is an important transportation policy concern. Today’s vertically integrated rail oligopolies (VIROs) in the United States, Canada, and Mexico have enabled rail freight to be commercially self-sustaining. A combination of favorable geography involving a choice of railroads for most longer hauls and commercial freedom for railroads to set prices without prior regulatory approval have helped create a situation in which North American freight railroads are self-sustaining without government subsidies. This research examines the development of VIROs in the United States, Canada, and Mexico today. A largely hands-off policy, combined with a willingness to allow railroads to accumulate enough money to maintain their physical plants to high standards, have led to today’s major freight railroad duopolies in the eastern and western United States, Canada, and Mexico. Despite some complaints from shipper interests, today’s VIROs are largely stable (with the possible exception of broader policy changes in Mexico). Lawmakers and regulators should ensure that any future mergers do not adversely affect the performance of what has thus far been a largely satisfactory model.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.352
Teacher spread0.268 · 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.

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

Citations4
Published2019
Admission routes1
Has abstractyes

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