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Record W2533049720 · doi:10.2166/wp.2016.004

Comparative analysis of water rights entitlements in India and China

2016· article· en· W2533049720 on OpenAlexaff
Shaofeng Jia, Yuanyuan Sun, Jesper Svensson, Maitreyee Mukherjee

Bibliographic record

VenueWater Policy · 2016
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcMaster University
FundersChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsChinaWater resourcesResource (disambiguation)Political scienceBusinessEnvironmental resource managementEnvironmental economicsEnvironmental planningEconomicsGeographyComputer scienceLawEcology

Abstract

fetched live from OpenAlex

Water rights are widely regarded as a crucial component to enhance efficient water use and for meeting a country's water resource challenges. This article presents a framework for analyzing and comparing the similarities as well as differences of the water rights systems between India and China. The article relies on the method of document research and comparative analysis to compare general characteristics of India and China's water rights systems based on six evaluation indicators and evaluation principles. Using this analytical framework, this paper compares the implementation effects of the water rights systems in terms of the degree of meeting water resources demand, conflict-resolution means and the protection of water resources. Our findings provide insights for the reformation of the water rights systems and bring out lessons that other developing countries can learn from India and China's experiences.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.007
GPT teacher head0.221
Teacher spread0.215 · 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
Published2016
Admission routes1
Has abstractyes

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