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Record W3173773552 · doi:10.1080/08865655.2021.1943493

Regional Cooperative Disaster Risk Management in Central Asian Borderlands

2021· article· en· W3173773552 on OpenAlexvenueno aff
N. G. Mavlyanova, В. А. Липатов, John P. Tiefenbacher

Bibliographic record

VenueJournal of Borderlands Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicTransboundary Water Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCentral asiaEmpowermentPolitical scienceNatural disasterEmergency managementEnvironmental planningEconomic growthRegional scienceGeographyBusinessInternational tradeEconomicsLaw

Abstract

fetched live from OpenAlex

This paper examines regional cooperation in disaster risk management (DRM) in the transboundary regions of five Central Asian states: Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. Regional cooperation to reduce disaster potential is a rather recent endeavour both internationally and in the region. Cooperation to enhance environmental security in post-Soviet Central Asia is slowly strengthening monitoring, planning, and prevention of natural disasters with a new approach that anticipates risks and hazards and seeks to reduce the likelihood of disasters instead of responding to the aftermath. Empowerment of regional associations to coordinate states’ activities to understand and solve common problems is needed. The legacy of the Soviet past and the contemporary states’ efforts to participate in regional cooperative organizations are reviewed and the prospects for new instruments for DRM cooperation are discussed. The needs are multifaceted and complex, but there are glimmers of promise for regional and borderland cooperation.

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 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 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 score0.671
Threshold uncertainty score0.584

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.315
Teacher spread0.287 · 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 teacher head, 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

Citations8
Published2021
Admission routes1
Has abstractyes

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