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Record W7101052999

TRANSPORTATION POLICY, COMPETITION AND ECONOMIC GROWTH *

2015· article· en· W7101052999 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
Fundersnot available
KeywordsCompetition (biology)ObstacleGovernment (linguistics)Economic welfareTransportation planningWelfarePublic transport
DOInot available

Abstract

fetched live from OpenAlex

Canadian transportation policy states that the best way to meet the needs of the Canadian economy and Canadians is to have a transportation system that is competitive, economic and efficient. The competitiveness of Canadian firms in Canadian, North American and world markets depends on the efficiency of the transportation system. The Canada Transportation Act (CTA) maintains the needs of users and the well being of Canadians in rural and urban Canada are met when transportation is provided in the most efficient way possible, namely, at the lowest total cost. The CTA is also clear in stating that the objectives of Canadian transportation policy in serving the needs of users and advancing the well-being of Canadians are achieved, among other things, when rates and conditions do not constitute an undue obstacle to the movement of traffic within Canada or for export and that competition and market forces, both within and among the various modes of transportation, are the prime agents in providing viable and effective transportation services. In essence, the CTA provides that transportation services should be supplied at rates that cover the cost of providing transportation services. Therefore, both the users of transportation services and the providers of transportation services will have their respective needs met by a “competitive, economic and efficient national transportation system”.1 While section 5 of the CTA is a clear statement of policy that competitive market forces should be the means of achieving the objectives of policy, the federal government has adopted differing strategies across modes for achieving those objectives. In rail and

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.255
Threshold uncertainty score0.508

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.108
GPT teacher head0.286
Teacher spread0.178 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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