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Record W2311479810 · doi:10.1093/publius/pjw002

Governing an International Transboundary River: Opportunism, Safeguards, and Drought Adaptation in the Rio Grande

2016· article· en· W2311479810 on OpenAlexaff
Dustin Garrick, Edella Schlager, Sergio Villamayor‐Tomás

Bibliographic record

VenuePublius The Journal of Federalism · 2016
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOpportunismEconomic shortageCorporate governanceWater scarcityAdaptation (eye)GeographyPolitical scienceEnvironmental resource managementEnvironmental planningBusinessGovernment (linguistics)EconomicsLaw

Abstract

fetched live from OpenAlex

We extend Bednar’s theory of a robust federation to examine the factors and institutions influencing the effectiveness of transboundary water governance in an international river basin. We examine the evolution and performance of drought adaptation in the Rio Grande/Bravo river basin of the United States and Mexico—two federal countries. Droughts and water shortages since 1990 have triggered opportunistic behavior by resource users and their governments. Analysis of case studies in three nested geographic contexts (internationally, interstate United States and interstate Mexico) generates evidence of opportunistic behavior and either limited or disputed compliance both internationally and within each country. Structural safeguards have stipulated powers and functions for water allocation and conflict resolution in all three settings, but roles and responsibilities are not clear during droughts. The limitations of structural, popular, and judicial safeguards have elevated the importance of joint monitoring, which we identify as a vital new form of safeguard.

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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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

Citations34
Published2016
Admission routes1
Has abstractyes

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