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Record W3188663700 · doi:10.1061/9780784483602.018

Evaluation of Public Private Partnership in Infrastructure Projects

2021· article· en· W3188663700 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.

Bibliographic record

VenuePipelines 2021 · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsGeneral partnershipProcurementPublic–private partnershipPlan (archaeology)BusinessPublic infrastructurePrivate sectorFinanceProcess managementRisk analysis (engineering)Engineering managementEngineeringMarketingEconomicsEconomic growth

Abstract

fetched live from OpenAlex

Public private partnership (PPP) has over the years proven to be a good procurement method for infrastructure projects. This partnership combines the efficiency, expertise, and innovation of the private sector as well as appropriate risk allocation. PPP provides an alternate avenue for capital needed for major engineering projects. The objectives of this paper are to highlight the effectiveness of implementing PPP by looking at past experiences in infrastructure projects, to investigate the conditions under which PPP is appropriate, and to identify the benefits, success, and difficulties of PPP. The results of this paper show that most experts in the infrastructure industry are aware of the effectiveness of PPP but are unable to determine how to maximize its success. This paper further identifies the various factors needed for a successful PPP, such as risk allocation and a good partnering plan for a successful execution of the project.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.110
GPT teacher head0.314
Teacher spread0.205 · 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