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Record W2313437367 · doi:10.5509/2011842245

Adversaries versus Partners: Urban Water Supply in the Philippines

2011· article· en· W2313437367 on OpenAlexaffvenue
Kate J. Neville

Bibliographic record

VenuePacific Affairs · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsUniversity of British Columbia
FundersTropical Resources InstituteUniversity of the PhilippinesYale University
KeywordsWater supplyBusinessEnvironmental planningNatural resource economicsWater resource managementGeographyEnvironmental scienceEconomicsEnvironmental engineering

Abstract

fetched live from OpenAlex

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.
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\nThis 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. 
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\n 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.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.275
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2011
Admission routes2
Has abstractyes

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