Models of Oil Exporting Countries’ Inclusion into Oil Refining Global Value Chains
Bibliographic record
Abstract
The main purpose of the research is to identify key models of oil exporting countries inclusion into oil refining global value chains. The countries possess high potential of integration into the processing sector with higher value added, but tend to implement it with different degrees of efficiency. Positive balance of foreign trade in refined oil products, calculated in value added terms, can be accompanied by dependence of country’s exports on foreign value added content, and negative balance can be explained by country’s imports of intermediate products with low level of processing to insure domestic production. Five of eight analyzed oil exporting countries show positive dynamics of inclusion into oil refining global value chains. The world biggest oil exporter, Saudi Arabia, doesn’t rely on foreign value added in its exports, whereas country’s forward participation index in global oil refining sector is very high. USA, Canada and Norway pursue specific models of integration into oil processing, which are developed in compliance with countries’ energy policies and aimed to create higher value added. Despite Kazakhstan dependence on Russian economy the country reduces foreign value-added content in its exports of oil refining products and improves participation in GVC. Two of the world leading oil exporters, Mexico and Brazil, demonstrate negative dynamics of inclusion into oil processing sector. High dependence of production on foreign value added, negative balance of foreign trade and poor integration into complex links within value chains are key parameters of ineffective GVC inclusion. The case of Russian Federation could be identified as positive integration with some obstacles. High volumes of Russian exports in oil refining products, positive trade balance in value added terms and high GVC forward participation index are accompanied by country’s increasing dependence on foreign value-added which forces Russia to rethink its participation in global refining sector and implement supporting policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".