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Record W2186402807 · doi:10.7202/1039114ar

Dreams, Deception and Delusion: The Derailing of Ottawa’s Light Rail Transit Plans

2017· article· en· W2186402807 on OpenAlexvenueaboutno aff
Robert Hilton, Christopher Stoney

Bibliographic record

VenueRevue Gouvernance · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsPoliticsScale (ratio)DeceptionService (business)Public serviceMarketingPublic relationsPublic administrationBusinessPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

In this article, the authors examine the failed City of Ottawa’s Light Rail Transit (LRT) project as a case study. While the LRT was trumpeted by local politicians and bureaucrats as a symbol of the city’s coming of age, the project was fraught with problems. The article explores these problems and points out that the way in which the project was managed is a cautionary tale in how not to promote large-scale public infrastructure projects. The authors point to the dangers that occur when those who make decisions about the expenditure of public funds become promoters of a project. There are serious risks when political commitments are made early on in a project’s development and appraisal stage. Rather than remaining focused on achieving levels of service within costs that are acceptable to those who pay for these services, decision makers can fall into the trap of ‘boosterism’that puts ego and status ahead of public interest.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.534

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0330.037
Scholarly communication0.0120.006
Open science0.0020.008
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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 designQualitative
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

Citations6
Published2017
Admission routes2
Has abstractyes

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