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“Neither Water, nor War:” the Problem of Fresh Water in International Relations of the First Quarter of the 21<sup>st</sup> Century

2024· article· en· W4393193572 on OpenAlexaboutno aff
Anastasia Likhacheva

Bibliographic record

VenueJournal of International Analytics · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)HistoryArchaeology

Abstract

fetched live from OpenAlex

In this article, the author compares the objective dynamics of changes in the state of the water problem since the beginning of the 21st century, which has continued to worsen in most regions of the world, and its foreign policy dimension, noting the transition from the global water alarmism of the 1990s to the challenges for small and medium powers, in solving the problems in which no major actor is yet actively interested. Fresh water, despite the slogans at the end of the last century about future water wars, the “blue oil” and the “new gold” of the 21ST century, has not become, and is unlikely to become, the cause of confrontation between great powers, while medium and small powers are still confine themselves to more traditional forms of conflict, including in the Tigris, the Euphrates and the Jordan basins, which have been seen for decades as a testing ground for future water wars. This does not negate the role of water bodies and ecosystems in promoting development or, conversely, in perpetuating the poverty and backwardness in entire regions. Waters remain in competition as an important source of water-intensive goods that can be converted into valuable assets – water-intensive goods, energy or the ability to achieve higher levels of the Human Development Index. And, of course, they can be used to exacerbate ethnic and political conflicts. This is where the potential of Russia, the world’s second water power, comes into play, both in terms of directly regulating regional water challenges and in influencing the approaches of international organizations and associations, in which Moscow plays a prominent role.

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)0.003
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: none
Teacher disagreement score0.016
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0130.027
Scholarly communication0.0160.013
Open science0.0010.006
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.247
Teacher spread0.237 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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