Bibliographic record
Abstract
In the Philippines, skepticism about private sector participation in urban water provision became increasingly pronounced as missed service targets and regulatory battles plagued governmental relations with the two companies (Manila Water and Maynilad) granted concessions for water provision in the capital, Manila. A comparative study of these two public-private partnerships (PPPs) reveals the challenges of reconciling bureaucratic and organizational dynamics with public suspicion of the private sector. This study draws on interviews and observations with corporate and government officials, academics, journalists, non-governmental organizations and civil society members in the Philippines, almost a decade after the initial privatization. This paper furthers our understanding of the outcomes in Manila -- and PPPs more generally -- by addressing the tension between credible commitment in contractual arrangements and flexibility for responding to economic and environmental shocks. It argues that adversarial interactions between the private corporations and regulators hindered the collaborative negotiations needed to respond to the currency crisis. Fear of public backlash against price increases and contract adjustments prevented the government and companies from engaging in meaningful joint problem solving. The differential outcomes of the companies illustrate the relevance of specific contractual arrangements and leadership in determining the impact of unforeseen shocks. However, the problems experienced by both companies indicates the need—if the private sector is to equitably and efficiently provide public goods—to redesign PPPs to increase transparency and to develop true partners.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".