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Record W2622537453 · doi:10.1177/2055563617710620

Breaking the path in national development? The politics of public–private partnerships in Ghana

2016· article· en· W2622537453 on OpenAlexaff
Joshua Jebuntie Zaato, Frank L. K. Ohemeng

Bibliographic record

VenueJournal of Strategic Contracting and Negotiation · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProcurementPoliticsGovernment (linguistics)Private sectorPath dependencyBusinessValue for moneyPublic sectorPublic–private partnershipValue (mathematics)Public infrastructureDebtPublic economicsPublic administrationEconomicsFinanceEconomic growthEconomic systemPolitical scienceGeneral partnershipMarketingEconomyComputer science

Abstract

fetched live from OpenAlex

Public–private partnerships (PPPs), it is argued, generate greater value for money than traditional procurement methods. Governments overwhelmed by budget deficits and public debts see them as a way to overcome the challenges of providing critical public infrastructure. However, many PPP projects are not on cost and on time, igniting criticisms and debate as to their merits. The Ghanaian Government has developed a PPP policy framework with the view of engaging the private sector to build needed infrastructure. Incorporating insights from path dependency, we are interested in finding out if, compared to traditional procurement, the policy represents a new path for national development.

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)0.006
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.010
Scholarly communication0.0080.009
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.000

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 head0.127
GPT teacher head0.286
Teacher spread0.160 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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