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Record W2790009468 · doi:10.1163/15692108-12341400

Evolution of the East Central Eurasian Hydrocarbon Energy Complex

2018· article· en· W2790009468 on OpenAlexaff
Robert M. Cutler

Bibliographic record

VenueAfrican and Asian Studies · 2018
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsCarleton University
Fundersnot available
KeywordsGeopoliticsCoherence (philosophical gambling strategy)Central asiaChinaForeign direct investmentMiddle EastGeographyEconomyEconomic geographyInvestment (military)Fossil fuelEast AsiaEnergy sectorPolitical scienceEconomic systemEconomicsPhysical geographyEcologyPolitics

Abstract

fetched live from OpenAlex

Abstract This article analyzes the evolution of the East Central Eurasian energy triangle China-Kazakhstan-Russia. It treats in depth the behavior of Chinese national oil companies ( noc s) regarding foreign direct investment ( fdi ) in Kazakhstan and Russia. The first section sets out the framework of geo-economics in a complexity-science perspective, in particular the key analytical categories of the ‘emergent coherence’ approach, and it defines a ‘hydrocarbon energy complex’ ( hec ). The second section analyzes the formation of the East Central Eurasian hec by examining Chinese energy investment in Kazakhstan and Russia since 1991, using the ‘emergent coherence’ framework as explained. The third section examines the geo-economics of Sino-Russian competition for energy resources in Central Asia, specifically in Kazakhstan and Turkmenistan, over the same timeframe. The fourth section presents conclusions about changes over time in the behavior of Chinese noc s regarding investment in Kazakhstan and Russia and cooperation with them in the energy sector. The fifth section, the conclusion, summarizes the findings, gives them geopolitical perspective, and concludes on the criteria determining the delimitation of the distinct chronological periods emerging from application of the ‘emergent coherence’ framework.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.960
Threshold uncertainty score0.617

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 studies0.0000.001
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 head0.022
GPT teacher head0.246
Teacher spread0.224 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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