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Record W3027373961 · doi:10.5539/jpl.v13n2p44

Is the European Migration Crisis Caused by Russian Hybrid Warfare?

2020· article· en· W3027373961 on OpenAlexvenueno aff
Viljar Veebel

Bibliographic record

VenueJournal of Politics and Law · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean and Russian Geopolitical Military Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeeContext (archaeology)European unionSolidarityPolitical scienceState (computer science)Political economyDevelopment economicsPoliticsEconomyInternational tradeGeographySociologyEconomicsLaw

Abstract

fetched live from OpenAlex

Recent developments in European security situation, starting with the Russia-Ukraine conflict, followed by the complicated Brexit and political instability in the Middle East and North Africa, have given rise to instability in the European Union. Yet, none of the other factors could be compared with the risks caused by the massive influx of refugees into the EU that challenges both solidarity and responsibility of the member states. In this context, it is extremely important to understand the actual security threats related to the refugee crisis and the root causes of growing refugee flows. This article discusses the roots of large-scale migration flows in the European Union (EU) over the present decade and investigates the potential link between migration flows and modern hybrid warfare, referring to the coordination of various modes of warfare, such as military and non-military means, conventional and non-conventional capabilities, state and non-state actors with an aim to cause instability and disarrangement. It is intriguing to investigate whether the increase in migration flows could be linked to present confrontation in the global arena on the Russia-West axis. Common patterns of migration flows from Syria and Ukraine to the EU are discussed, as well as policy recommendations are given to diminish the negative impact of similar events in the future.

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)0.000
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: none
Teacher disagreement score0.902
Threshold uncertainty score0.332

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.024
GPT teacher head0.273
Teacher spread0.250 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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