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Record W2765959565 · doi:10.1177/1476750317736370

Taking the road less taken: reorienting the state for periurban water security

2017· article· en· W2765959565 on OpenAlexfundno aff
Vishal Narain, Pranay Ranjan, Sumit Vij, Aman Dewan

Bibliographic record

VenueAction Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsDistrustService providerProvisioningBureaucracyWater securityResilience (materials science)BusinessDilemmaService (business)Environmental resource managementWater resourcesPolitical scienceMarketingEconomicsComputer science

Abstract

fetched live from OpenAlex

This paper describes the intervention strategy to improve water security in Sultanpur, a village in periurban Gurgaon, India. Most approaches to improving natural resource management in periurban contexts focus on mobilising the community; little attention is paid to reorienting the state or strengthening the user-bureaucracy interface. This paper describes the action research process that was followed to reorient civic agencies engaged in the provisioning of water and to break from a situation of distrust and prisoners' dilemma between water users and service providers. The paper argues that the creation and provision of a platform for direct engagement between water users and service providers can be a key tool for improving periurban water security. These platforms can provide support in building community resilience to face challenges such as climate variability and urbanisation, both of which threaten periurban water security. The action research emphasises on building the community's capacity to ask for improved water supply and to negotiate with state service providers, rather than augmenting water supply physically.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.011
Scholarly communication0.0060.007
Open science0.0010.010
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.201
GPT teacher head0.467
Teacher spread0.266 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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