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Record W2949383443 · doi:10.1089/sus.2019.0007

Investigating the Institutional Landscape for Urban Water Security in Nepal

2019· article· en· W2949383443 on OpenAlexfundno aff
Chandra Lal Pandey, Gyanu Maskey, Kamal Devkota, Hemant Ojha

Bibliographic record

VenueSustainability The Journal of Record · 2019
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsWater securityWater scarcityUrbanizationWater resourcesPopulationClimate changeScarcityBusinessWater supplyFood securityEnvironmental planningNatural resource economicsGeographyDevelopment economicsPolitical scienceEconomic growthAgricultureEconomicsEnvironmental scienceEcologyEnvironmental engineeringSociologyMarket economy

Abstract

fetched live from OpenAlex

Abstract Achieving water security is one of the major global challenges in the age of climate change, urbanization, rapid population increase, and weak water institutions. Despite the proliferation of water institutions and policies at national and local levels, the slow response to address water scarcity remains a puzzle in Nepal. This study investigated the state of water insecurity in relation to institutional structures, particularly focusing at the local level in Nepal. A qualitative research approach was used in two case study cities: Dhulikhel in central Nepal and Dharan in the east. The study found that failing to achieve water security is not due to a lack of an abundant supply of physical water in the country; rather, the problem is more about resolving the institutional complexity resulting from the existence of multiple water institutions with overlapping and competing roles and responsibilities. The authors conclude that strengthening institutional capacity is the key, including some fundamental rethinking to ensure clearly articulated and complementary roles, responsibilities, and relationships.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.158

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2019
Admission routes1
Has abstractyes

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