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Record W2951001777 · doi:10.1515/rne-2019-0016

The Supply Network and Price Dispersion in the Canadian Gasoline Market

2018· article· en· W2951001777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueReview of Network Economics · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsPrice dispersionTruckGasolinePipeline transportAgricultural economicsDispersion (optics)Pipeline (software)EconomicsUnit priceBusinessEnvironmental scienceEconometricsMicroeconomicsEnvironmental engineeringAutomotive engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract This paper examines the impact of variation in transportation options – what I denote the “supply network” – on observed price differences between locations for a specific good, retail gasoline. I use a unique data set of weekly gasoline prices across 44 Canadian cities to analyze how the existence of variation in the available modes of transportation for gasoline between cities (via pipeline, marine tanker, rail or truck) accounts for observed price differences across locations. I find that the supply network is significant – cities connected by lower cost-per-unit methods like pipelines or seaports exhibit smaller mean- and weekly-price differences than those connected only by road or rail, after controlling for distance, regional effects and market size. A pipeline connection results in a reduction in weekly price dispersion equivalent to a 53% reduction in distance between cities, while a maritime connection has the equivalent effect of a 38% reduction in distance between cities.

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.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.543
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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