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Record W4402342889 · doi:10.29117/irl.2024.0289

Heterogeneous Diplomacy of Political Relations between Iran and Afghanistan in exploiting the water of Helmand Border River

2024· article· en· W4402342889 on OpenAlexaff
Abbas Poorhashemi, Sobhan Tayebi, Marzieh Fathi Bornaji

Bibliographic record

VenueInternational Review of Law · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Conflicts in Afghanistan, Pakistan, and Middle East
Canadian institutionsCanadian Council on International Law
Fundersnot available
KeywordsDiplomacyPoliticsPolitical scienceGeographyWater resource managementBiologyEnvironmental scienceLaw

Abstract

fetched live from OpenAlex

The water challenge in the Middle East has caused governments to face new problems in which they have to consider more control, management, and care regarding their water resources to survive. Due to growing water consumption by upstream countries of the border rivers in exploiting the resources, we will observe an increase in hydro-political disputes among the nations. Helmand is a river that flows into Iran and includes a hydro-political aspect. It has always influenced the relations between the two countries since the border formation. The fluctuation of the Helmand water flow and decrease in the running water towards Sistan (Iran) in the last hundred years has always created problems in relations between Iran and Afghanistan at the National and local levels. In the past years, Afghanistan has consumed a significant part of the water by creating a dam, reducing the amount of water flowing into Iran. On the other side, morphological changes of the river, such as the dispute on specifying the border exactly in the changeable river bed, dispute from the instability of water rights distribution pattern between the two countries, and finally, the dispute from specifying the extent and territory of the border area has led to ecosystem instabilities. In this regard, the strategy to control the ecosystem instabilities is environment diplomacy, and if it is synchronized with defensive diplomacy, it causes stability. With this description, we will examine the Helmand peripheral issues and the course of diplomacy in this field.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.040
GPT teacher head0.386
Teacher spread0.346 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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