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Record W3035829828 · doi:10.1596/978-1-4648-1574-4_ch2

Definition, Characteristics, and Types of Health PPPs

2020· book-chapter· en· W3035829828 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe World Bank eBooks · 2020
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicPublic-Private Partnership Projects
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyBusiness

Abstract

fetched live from OpenAlex

Develops a taxonomy of the different types of public-private partnerships (PPPs), describes their key characteristics, and suggests some conditions for successful implementation. While PPPs comprise a distinct subset of public-private engagements (PPEs), they are not the same as privatization; a key feature of a PPP involves the sharing of both risks and responsibility by the public sector and the private sector. Health PPPs were first implemented in high-income countries in the 1990s and then spread across middle- and low-income countries; PPPs in the health sector tend to focus on the construction, maintenance, or both of health care infrastructure and service delivery. Five types of health PPPs include managed equipment services (MES), operation and maintenance (O&M) services, specialized services, health facility, and integrated PPP. Some countries (including Australia, Canada, Japan, and the United Kingdom) define and describe their PPPs by the functions transferred to the private sector; other countries focus on legal ownership and control of assets in PPPs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

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

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 head0.063
GPT teacher head0.243
Teacher spread0.180 · 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