Public-Private Partnership Arrangements for Boosting the Supply of Affordable, Adequate and Quality Houses: Lessons for Papua New Guinea
Bibliographic record
Abstract
Providing adequate, quality and affordable housing for its population have been a long-standing issue for governments of some countries. To address the issue, some of the governments have adopted Public-Private Partnership (PPP) with the hope of improving housing delivery. However, identifying the most suitable PPP arrangement for providing houses is often problematic. This paper reports on a study of the different PPP arrangements that can be applied in housing delivery, critical success factors and challenges associated with the arrangements. It also reports lessons that Papua New Guinea (PNG) can draw from other countries that have applied PPP extensively in housing delivery. The study is based on a historical narrative literature review that was analysed using manifest qualitative content analysis. The findings revealed that there are several PPP arrangements that can be used in providing houses such as the direct relationship ownership housing which is similar to the build-lease-operate-transfer. Another type of arrangement is the direct relationship rental housing which is similar to the build-own-operate. Critical success factors for a PPP project include the need for transparency at all stages of the PPP, risks must be allocated properly between the public sector and the private sector, the PPP should have adequate political and community support. The performance of a PPP arrangement can be restricted by high transaction costs, poor contracting and procurement procedures, the dominance of the public sector in the arrangement, poor communication between the partners and inadequate legal frameworks. The lessons that PNG can draw from other countries include the identification of PPP arrangements that are most suitable for the country and how to implement the arrangements in an effective manner. The findings provide more understanding on the application of PPP arrangements in housing delivery by considering the challenges and success factors associated with the arrangements.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".