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Record W2139222717 · doi:10.1111/apce.12094

THE MUDDLING CROWN: VIA RAIL AND THE FEDERAL GOVERNMENT

2015· article· en· W2139222717 on OpenAlexaffabout
Malcolm G. Bird

Bibliographic record

VenueAnnals of Public and Cooperative Economics · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsSubsidyGovernment (linguistics)PoliticsState (computer science)BusinessService (business)Corporate governancePublic administrationEconomicsMarket economyFinanceMarketingPolitical scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT This article is an analysis of VIA Rail and its relationship with the federal government. Canada's publicly‐owned intercity passenger rail service is in a state of slow and steady decline, best illustrated by its small and falling user rates, and despite receiving significant subsidies, the federal government is indifferent to the needs of this transportation provider. Unlike other Canadian state‐owned enterprises, or Crown corporations, VIA Rail has been neither privatized nor modernized and, instead, is languishing as a publicly‐owned firm. While the current Conservative government of Stephen Harper favors a smaller role for the state, and it has both set about modernizing and eliminating other Crowns, it has not followed such a course with VIA Rail. Drawing on John Kingdon's (1984) multiple streams model to outline the empirical data as well as to illustrate how this firm interacts with its political superior, it will argue that the indifference towards this firm is due to both the specific characteristics of VIA Rail and the highly centralized decision‐making structure of Canada's Westminster Parliamentary system. Unless the federal government decides to implement fundamental organizational and governance changes at this firm, Canadians will continue to be served by a marginal rail service provider.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.066
GPT teacher head0.238
Teacher spread0.172 · 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.

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

Citations4
Published2015
Admission routes2
Has abstractyes

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