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Record W2125408546 · doi:10.1068/a44616

Good Water Governance without Good Urban Governance? Regulation, Service Delivery Models, and Local Government

2012· article· en· W2125408546 on OpenAlexaffabout
Kathryn Furlong

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCorporate governanceService delivery frameworkGovernment (linguistics)Water industryBusinessWater supplyPublic administrationLocal governmentService (business)Political scienceFinanceMarketing

Abstract

fetched live from OpenAlex

‘State failure’ came to prominence in the 1980s to explain a range of challenges facing water supplies. Given the apparent problem, water supply was said to require organizational reform which would reduce government involvement in and influence over service delivery. Service providers, it was argued, should be independent from government. Among the associated reforms privatization has drawn the most attention, but alternative service delivery (ASD) has also proven important. Concomitantly, the regulatory role of senior governments was initially ‘rolled back’. Since that time, regulatory oversight at higher scales has been reasserted in many cases, yet the perceived need to circumscribe the role of municipal governments through organizational reforms like ASD persists. Using a case study of water sector reform in Ontario, Canada, I argue that such views conflate organizations with governance, thus ignoring underlying municipal issues affecting water supply. This, in turn, can limit the effectiveness of regulatory improvements at higher scales. Given the increased focus on institutions to resolve water-supply challenges, these findings have implications for other contexts. In Canada a municipality is a local government whose powers and responsibilities are defined by the provinces under their respective municipal acts. While these powers are typically limited compared with other jurisdictions, in keeping with trends elsewhere municipal responsibilities have been increasing.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.596
Threshold uncertainty score0.812

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.033
Scholarly communication0.0100.006
Open science0.0010.003
Research integrity0.0020.002
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 head0.192
Teacher spread0.182 · 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

Citations20
Published2012
Admission routes2
Has abstractyes

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