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Record W2323374361 · doi:10.1061/40927(243)213

Strategic Insights into the Jordan River Conflict

2007· article· en· W2323374361 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWorld Environmental and Water Resources Congress 2007 · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicGame Theory and Applications
Canadian institutionsUniversity of Waterloo
FundersUniversity of California, DavisUniversity of Waterloo
KeywordsConflict resolutionWin-win gameGame theoryPoliticsConflict analysisSocial conflictManagement scienceResolution (logic)Value (mathematics)Operations researchComputer scienceEconomicsPolitical scienceMicroeconomicsEngineeringArtificial intelligenceLaw

Abstract

fetched live from OpenAlex

This study illustrates the value of game theoretic methods in water resources conflict resolution by the application of the Graph Model for Conflict Resolution (GMCR) to the Jordan River Conflict. The major objective of the paper is to show how GMCR can be readily applied to virtually any social conflict. The GMCR II decision support system, with its unique ability to handle socio-political conflicts, is used to model and analyze a generic version of this ongoing Middle-East conflict. The game model finds the equilibria of the game and suggests possible coalitions of conflicting parties.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.999

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.282
Teacher spread0.245 · 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