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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.193

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
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