MétaCan
Menu
Back to cohort
Record W2910854604 · doi:10.1177/0361198118823005

Analysis of Private Participation Effects in Bus Rapid Transit Projects in Ecuador

2019· article· en· W2910854604 on OpenAlexaff
Juan F. Arias, Chris Bachmann

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBus rapid transitPublic transportBusinessPrivate enterpriseSustainable transportTransport engineeringFunction (biology)Transit (satellite)Private transportPrivate sectorFinanceTransit systemEngineeringEconomic growthEconomicsSustainability

Abstract

fetched live from OpenAlex

The two largest cities in Ecuador each implemented three bus rapid transit (BRT) corridors from 1995 to 2013. The projects present similar characteristics, and thus a unique opportunity to analyze factors that influenced their performance. This paper identifies the level of private participation and the extent to which it influenced the outcomes of the projects. Two approaches were identified: 1) including incumbent operators by delegating vehicle acquisitions and operations; and 2) replacing them with a public company. The financial strength and interest of the incumbent operators in continuing to function along parallel routes were major issues. Quito was successful in the implementation of the first corridor through public delivery but failed in its attempts with private participation. Guayaquil surmounted the barriers for effective private participation through a special purpose vehicle (SPV) that managed the risk created by the inherent nature of the consortiums. It is expected that a deeper understanding of these processes will contribute to more efficient and sustainable transportation investments in Ecuador.

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)0.013
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.403
Teacher spread0.332 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Record Journal of the Transportation Research BoardSame topicTransportation Planning and OptimizationFrench-language works237,207