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Record W2039990686 · doi:10.3167/ares.2010.010103

Neoliberal Water Management: Trends, Limitations, Reformulations

2010· article· en· W2039990686 on OpenAlexafffund
Kathryn Furlong

Bibliographic record

VenueEnvironment and Society · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de MontréalEuropean CommissionStockholm Environment Institute
KeywordsCorporate governanceNeoliberalism (international relations)Water sectorWater industryPrivate sectorPolitical scienceEconomicsAdministration (probate law)Public administrationBusinessPolitical economyWater supplyEconomic growthManagementEngineering

Abstract

fetched live from OpenAlex

The impact of neoliberal policy reform on water management has been a topic of significant debate since the mid-1980s. On one side, a number of organizations have generated an abundant literature in support of neoliberal reforms to solve a range of water governance challenges. To improve water efficiency, allocation, and management, supporters have advocated the introduction and/or strengthening of market mechanisms, private sector ownership and operation, and business-like administration. Other individuals and groups have responded critically to the prescribed reforms, which rarely delivered the predicted results or became fully actualized. This article endeavors to articulate the varying sets of claims, to analyze the trends, to test them against their forecasted benefits, and to examine certain prominent proposals for reforming the reforms. The water sector experience with neoliberalization reveals several sets of contradictions within the neoliberal program, and these are discussed in the final section of the article.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0020.015
Scholarly communication0.0090.011
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations46
Published2010
Admission routes2
Has abstractyes

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