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Record W2607263232 · doi:10.7202/1039118ar

Transformative Change and Measuring Success: Public-Private Partnerships in British Columbia, 2001-2005

2017· article· en· W2607263232 on OpenAlexaffvenueabout
Daniel Cohn

Bibliographic record

VenueRevue Gouvernance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsGeneral partnershipObligationProcurementPublic relationsBusinessAgency (philosophy)Private sectorTransformative learningPublic–private partnershipPublic administrationMarketingFinanceEconomicsPolitical scienceSociologyLawEconomic growth

Abstract

fetched live from OpenAlex

Public-private partnerships can be understood to be instruments for meeting the obligations of the state (things that there has been a strong social consensus that the state ought to do) that are transformed so as to involve private property ownership as a key element in the operation of the instrument. The word partnership is important and not just a euphemism for hiding a privatization (at least it ought not to be). Partnership means a relationship based on common goals where both entities share benefits and contribute resources over the long-term for mutual advantage and out of a sense of commitment. In a design-build-finance-operate (DBFO) public-private partnership, the state agency sponsoring the development hires either a single company (or consortium of companies) to, as the term suggests, meet the full extent of its public obligation by determining how best to meet the obligation, designing and building the necessary infrastructure and then operating it. This paper looks at the development of three DBFO public-private partnerships in and around Vancouver, British Columbia, asking what the likelihood is that the public will benefit from decisions to employ this procurement model. When using a definition of benefit that is broader than simply saving money, it is possible that these projects can provide greater benefits than a traditional public procurement, although the managers of two of the projects will likely face greater difficulties in doing so than the managers of the third one.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.658

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0080.003
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0010.002
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 head0.146
GPT teacher head0.259
Teacher spread0.112 · 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

Citations4
Published2017
Admission routes3
Has abstractyes

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Same venueRevue GouvernanceSame topicPublic-Private Partnership ProjectsFrench-language works237,207