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Record W4403309774 · doi:10.1016/j.retrec.2024.101484

Switch it: Canadian rail regulations, Ramsey pricing, and potential implications for U.S. rail policy

2024· article· en· W4403309774 on OpenAlexafffundabout
James Nolan, Hakan Andic

Bibliographic record

VenueResearch in Transportation Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsUniversity of Saskatchewan
FundersCollege of Agriculture and Bioresources, University of Saskatchewan
KeywordsBusinessEconomicsTransport engineeringEngineering

Abstract

fetched live from OpenAlex

Like the Surface Transportation Board (STB) in the United States, the Canadian Transportation Agency (CTA) is the regulatory body governing the rail sector within Canada. Due to possible policy relevance, we examine the CTA’s regulated zonal interswitching (broadly equivalent to reciprocal switching in the U.S.) rates from the perspective of Ramsey (second best) pricing. Interswitching in Canada is intended to promote inter-rail competition when two or more railroads are proximate to each other and the shipper. Using Canadian waybill data and associated pricing parameter estimates as input into a numerical simulation, we examine extant Canadian interswitching rates in comparison to Ramsey rates, assuming each zonal rate corresponds to a distinct level of shipper demand. We find that recent Canadian interswitching rates are not far off of comparable Ramsey prices. Regarding U.S. policy, our findings imply that Ramsey pricing principles could still be used to set reciprocal switching access rates that would be economically justifiable to both shipper and carrier.

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)0.003
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.942
Threshold uncertainty score0.421

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0030.003
Scholarly communication0.0050.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.059
GPT teacher head0.321
Teacher spread0.262 · 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 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

Citations1
Published2024
Admission routes3
Has abstractyes

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