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Record W2052057941 · doi:10.3141/1785-02

Buying into Amtrak: One Way to Fit American Railroads into Government’s Transportation Spending

2002· article· en· W2052057941 on OpenAlexaff
Anthony Perl

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRevenueFinanceTrainRestructuringBusinessPublic transportTax revenueFuel taxExcisePunctualityGovernment (linguistics)MandateCorporationRevenue sharingTransport engineeringEconomicsPublic economicsEngineering

Abstract

fetched live from OpenAlex

The nearly certain failure of the National Railroad Passenger Corporation (Amtrak) to meet a legislated mandate of attaining commercial self-sufficiency in 2002 and the major reassessment of transportation policies precipitated by America’s war on terrorism have opened a window of opportunity to improve the terms by which passenger trains fit into U.S. transportation policy. The future success of intercity passenger rail depends on going back to the drawing board of transportation policy to revisit railroads’ access to public finance. A major change from the public policy status quo on the rail mode’s financing recommends restructuring Amtrak to become a joint venture between government and privately owned railroads, as was envisioned by the original Rail Passenger Service Act of 1970. This change in corporate ownership would occur parallel with, and facilitate the implementation of, a new account in the transportation trust fund devoted to improvements in intercity rail infrastructure. This account would receive the tax revenues collected from some combination of federal excise taxes levied on railroad diesel fuel, other transportation expenditure by railroads (e.g., parts and equipment), passenger tickets, and express freight shipments. A share of railroads’ income taxes (or other sources of public revenue) could supplement the public revenues flowing into such a fund. Expenditures would be made on upgrading rail infrastructure capacity to handle increasing volumes of freight and passenger traffic and, where appropriate, to develop dedicated high-speed passenger rail infrastructure alongside existing rail rights-of-way.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0060.004
Scholarly communication0.0090.009
Open science0.0010.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0210.003

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 head0.109
GPT teacher head0.338
Teacher spread0.229 · 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 designTheoretical or conceptual
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

Citations2
Published2002
Admission routes1
Has abstractyes

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