Public Private Partnerships (PPPs) and Enhanced Service Delivery in Uganda: Implications from the Energy Sector
Bibliographic record
Abstract
This paper focuses on the use of Public Private Partnerships (PPPs) as a strategy to address deficiencies in the energy sector of Uganda in order to remedy the power generation shortage in the country. Public Private Partnerships have become popular and gained wide adoption in public sector management though with varying degrees of success especially in Africa. This paper borrows from the transactional theory to help examine the contractual structure, assets specificity, and comparative costs of buying decision making in the Public Private Partnership in the energy sector. The paper also borrows from the stakeholders’ theory as it highlights the need to identify and establish the different stakeholders in the Public Private Partnerships (PPPs) in the energy sector. It highlights the common concepts and forms of Public Private Partnerships in utilities; presents two case experiences of PPP in the energy generation of Uganda and lessons learnt. A review of the two case studies suggests a number of learning points related to involvement of stakeholders, need for government monitoring of the Public Private Partnership contracts and fostering of a win-win outcome. The paper highlights that successful implementation of a PPP depends to a large extent, on the development of capacity, sound legal procedures, agreements, and contracts that clearly define the relationship between government agencies and private firms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".