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Record W2032234481 · doi:10.1163/15691497-12341323

Chinese Energy Companies’ Relations with Russia and Kazakhstan

2014· article· en· W2032234481 on OpenAlexaffabout
Robert M. Cutler

Bibliographic record

VenuePerspectives on Global Development and Technology · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsForeign direct investmentSection (typography)SchematicIncentiveSoviet unionPolitical scienceResource (disambiguation)ChinaEconomyEconomic systemBusinessEconomicsMarket economyEngineeringPoliticsLaw

Abstract

fetched live from OpenAlex

This article concerns Chinese energy relations with Kazakhstan and Russia, surveying the foreign direct investment (fdi) behavior of Chinese National Oil Companies (nocs) in Kazakhstan and Russia. The first section provides a schematic overview of the general development of Chinese fdi strategy and behavior from the disintegration of the Soviet Union up until the present day. It systematically explains how that development occurred in three phases and gives some key indicators for distinguishing among the phases and the transition between successive phases. The second section looks more closely at the fdi strategy and behavior of the Chinese nocs specifically regarding Kazakhstan and Russia, periodizing it according to the first two of the three chronological phases distinguished in the first section. The third section of the article examines still more closely the phenomenon regarding Kazakhstan and Russia from the end of the last decade up until the present day, dividing the third above-mentioned phase into three subphases and inspecting the first two, of which the second is still ongoing. The fourth section of the article evaluates the conduct of Chinese nocs with regard to Kazakhstan and Russia from the standpoint of motives of corporate behavior and comparative incentive structures. The fifth section of the article concludes by introducing some caveats on the basis of a glance at recent behavior with respect to another large resource-rich country, Canada, where Chinese nocs have made massive fdi for some years now, but which has a rather different economic and social structure from Kazakhstan and Russia. The last section of the conclusion also includes a few final comments on the prospects for Chinese energy and Chinese nocs during the remainder of the decade and into the 2020s, on the basis of the analytical framework employed to structure the narrative analysis in the body of the article.

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)0.000
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.268
Teacher spread0.262 · 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

Citations8
Published2014
Admission routes2
Has abstractyes

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