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Record W4412761752 · doi:10.1002/wwp2.70018

Water Diplomacy in the Cauvery River and Mullaperiyar Dam: A Case Study of Tamil Nadu's Experience With Karnataka and Kerala

2025· article· en· W4412761752 on OpenAlexaff
Dhanabalan Thangam, Pradeep Kumar Shinde, Sriram Ananthan, Devarajanayaka Kalenahalli Muniyanayaka, Thirupathi Manickam, Sathis Kumar G.

Bibliographic record

VenueWorld Water Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsYorkville University
Fundersnot available
KeywordsTamilDiplomacyGeographyWater resource managementSocioeconomicsPolitical scienceEnvironmental scienceSociologyArtLaw

Abstract

fetched live from OpenAlex

ABSTRACT Water sharing by multiple nations internationally often leads to issues concerning access, utilization, and sustainability. In South India, Tamil Nadu's incident with water diplomacy, mainly in managing the Cauvery River Basin shared with Karnataka, presents important implications for reserve management and conflict resolution. The state relies heavily on its river systems for irrigation, drinking water, and industrial use, but the scarcity and irregular allocation of water resources pose a significant challenge. Efficient water diplomacy can help achieve sustainable water administration by fostering common thought and cooperation among riparian states. This paper critically evaluates the challenges and opportunities in Tamil Nadu's water peacekeeping, focusing on its commitment to neighboring states over collective water resources. It explores key themes such as the historical context of interstate water‐sharing disagreements, the efficiency of existing lawful and institutional frameworks, and the role of political and social arrangements in shaping water‐sharing negotiations. The paper also underscores the need for a more practical and mutual approach to water distribution, moving away from legal arbitration and political negotiation to embrace the values of sustainable and evenhanded water management.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.326
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.309
Teacher spread0.295 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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