Conceptual Framework for a Decision-Making Model to Select thePublic-Private Partnership (PPP) Structure for Urban Rail Transit inJakarta
Bibliographic record
Abstract
PPP scheme has been widely recognized as a cooperation between government and private sector to provide public service, both in developed and developing countries. In contrast with developed countries, PPP implementation faces several challenges in developing countries. Private sectors in developing countries tend to deliver an unsolicited PPP project; however, it is difficult for the government to define the appropriate PPP structure for a specific project due to lack of knowledge and experience. This paper focuses on developing a conceptual framework for a decision-making model to select the PPP structure model for urban rail transit. A clearly defined PPP structure at the early stages allows both parties' allocation of responsibility and risk. It provides exact direction on the PPP project implementation, commonly established in a long-term contract period. It is expected that the multi-criteria decision-making tools by using the AHP approach may contribute as a guideline to support decision-makers in selecting the appropriate PPP structure for urban rail transit, particularly on unsolicited PPP proposal.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".