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Record W4392362076 · doi:10.54097/q4t47x64

The Current Situation of Energy Trade Between Russia and the EU: From the Perspective of Economic Nationalism

2024· article· en· W4392362076 on OpenAlexaff
Runyu Li, Junjie Qin, Lingsu Yu

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNationalismPerspective (graphical)Economic nationalismPolitical scienceInternational tradeEnergy (signal processing)Current (fluid)Economic systemInternational economicsEconomicsPoliticsPhysicsArtLaw

Abstract

fetched live from OpenAlex

Energy trade has been a long-standing pillar of EU-Russia relations and a critical economic driver, leading to higher interdependence in the energy sector. With the outbreak of the Russia-Ukraine crisis, the energy relations between the EU and Russia and the development of the energy trade have changed, which also generates different energy trade policies and the current state of the energy trade. For a long time, people tend to pay attention to the political nature of nationalism and ignore its economic and cultural nature; they pay attention to the study of energy issues from the economic perspective and neglect the comprehensive study of energy issues from the political, cultural, and even nationalist perspectives. Synthesis, multiple factors, and the interaction of national security in the overall national security situation are significant features of the nationalist-economist perspective. This paper will explore the EU-Russia energy trade dealings from an economic nationalist perspective, focusing on the current state of energy relations and energy policies. Analyzing the current state of cooperation and trade in Russian-EU energy trade from this perspective can fill the gaps in the political and economic interactivity of Russian-EU relations in related studies.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score1.000

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 studies0.0010.001
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 head0.045
GPT teacher head0.333
Teacher spread0.288 · 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 designTheoretical or conceptual
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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