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Record W3097650481 · doi:10.22215/etd/2014-10425

Building State Infrastructure Privately: The Emergence and Diffusion of Public Private Partnerships in Canada, the United Kingdom and the United States of America

2014· dissertation· en· W3097650481 on OpenAlexaboutno aff
Christian Bordeleau

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsKingdomState (computer science)Private sectorPoliticsHistory of the United StatesPolitical scienceEconomicsPublic administrationEconomyEconomic growthEconomic historyLaw

Abstract

fetched live from OpenAlex

This study introduces a new explanation of the creation and expansion of public-private partnerships (PPP’s) in developed countries, using Canada, the United States and the United Kingdom as case studies. While classical accounts of the rise of PPP’s have highlighted the fiscal constraints of states, superior efficiency of PPPs, particular political parties and the long history of governments’ cooperation with the private sector, none of those accounts can explain the emergence of PPPs at a specific time in history and the cross-jurisdictional variation in the timing of the diffusion. Using a policy diffusion perspective, the thesis examines the historical evolution of project finance in the private sector starting in the 1930s in Texas’s oil prospecting venture industry. Taking into account this evolution and the associated evolution of supporting institutions over time helps explain better the specific timing of the birth of PPP’s in the United Kingdom, the United States and Canada.

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)0.001
metaresearch head score (Gemma)0.009
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 score0.091
Threshold uncertainty score0.657

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0080.005
Scholarly communication0.0070.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.255
Teacher spread0.221 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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