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Record W2765285402 · doi:10.1163/23529369-12340008

Transboundary Waters, Infrastructure Development and Public Private Partnership

2017· article· en· W2765285402 on OpenAlexaff
Richard Kyle Paisley, Riley T. Denoon, Theressa Etmanski, Patrick Weiler

Bibliographic record

VenueBrill Research Perspectives in International Water Law · 2017
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsCentre for International Governance InnovationUniversity of British Columbia
FundersEuropean Investment BankMedical Research Council
KeywordsGeneral partnershipHydroelectricityContext (archaeology)BusinessPublic–private partnershipPrivate sectorInvestment (military)FinanceCritical infrastructureFunction (biology)Environmental planningEconomicsEconomic growthPolitical scienceEngineeringPoliticsGeography

Abstract

fetched live from OpenAlex

Abstract Governments increasingly look to the private sector for the financing, design, construction, and operation of infrastructure projects, and as a result, public-private partnerships ( ppp s) have emerged as a valuable source for investment funds and expertise. ppp s involving new uses of transboundary waters require giving particular attention to the huge potential for environmental and social impacts. This monograph examines what ppp s are and how they, and environmental and social ‘safeguards,’ function in a transboundary waters context and with each other. This examination is undertaken through the prism of the Nam Theun 2 and Xayaburi hydroelectric power projects in Lao pdr . This monograph discusses and draws some important lessons from these ppp s contractual arrangements, costs, financing, and risk mitigation, for ppp s to be contemplated in other transboundary waters contexts.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.012
Scholarly communication0.0110.007
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.061
GPT teacher head0.323
Teacher spread0.262 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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