Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".