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Record W2993369861 · doi:10.20377/cgn-80

Transboundary Water Governance in the Kabul River Basin: Implementing Environmental and Public Diplomacy Between Pakistan and Afghanistan

2019· article· en· W2993369861 on OpenAlexaff
Bindu Panikkar, Asim Zia, S. Sgorbati, Michael E. Cohen, Muhammand Abid, Muhammad Fayyaz, Nadia Hashimi, Shaukat Ali, Monir Ahmad, Zuhra Aman, Suleiman Halasah, D. P. Rice, Gemma Del Rossi, Benjamin E.K. Ryan, Kashif Hameed, Mujahid Hussain, Naeem Salimee

Bibliographic record

VenueComplexity Governance & Networks · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsDawson CollegeAga Khan Foundation
Fundersnot available
KeywordsDiplomacyPolitical scienceRiparian zoneCorporate governanceCivil societyEnvironmental governanceEnvironmental planningPublic participationStewardship (theology)GeographyEnvironmental protectionPublic administrationBusinessEcology

Abstract

fetched live from OpenAlex

This research highlights the outcomes of the environmental diplomacy workshop held between members of civil society from Afghanistan and Pakistan on water cooperation in the Kabul River Basin, one of the most heavily conflicted transboundary river basins in the world. Lack of trust among these upstream and downstream riparian partners and persistent failures of Track 1 diplomacy initiatives has led to an absence of governance mechanisms for mitigating the water security concerns in the region. This research shows that science and public diplomacy, democratic participation, and social learning may pave a way to clear local misconceptions, improve transboundary water cooperation, and increase ecological stewardship in the Kabul River Basin.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.004
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.265
Teacher spread0.243 · 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 designNot applicable
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

Citations6
Published2019
Admission routes1
Has abstractyes

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