The Crude Oil International Trade Competition Networks: Evolution Trends and Estimating Potential Competition Links
Bibliographic record
Abstract
In the context of the economic situation, international relations, and the consequences of COVID-19, the future competition pattern of crude oil trade is uncertain. In this paper, the crude oil international import competition and export competition networks are based on a complex network model. The link prediction method is used to construct a crude oil competition relationship prediction model. We summarize the evolving characteristics of the competitive landscape of the global crude oil trade from 2000 to 2019 and explore the reasons for the changes. Finally, we forecast the future potential crude oil import and export competition. The results indicate the following. (1) The crude oil import competition center is transferred from Europe and America to the Asia–Pacific region and it may continue to shift to developing regions. (2) At present, the competition among traditional crude oil exporters is the core of crude oil export competition, such as OPEC, Canada, and Russia. The United States has become the world’s largest crude oil exporter, which means that the core of crude oil export competition has begun to shift to emerging countries. The competition intensity of emerging crude oil exporters is gradually increasing. There is likely to be fierce export competition between traditional and emerging exporters. (3) In the future crude oil competition, we should pay attention to the trend of the United States, which may lead to the restructuring of the global oil trade pattern. Finally, this paper considers the exporters and importers and puts forward policy suggestions for policymakers to deal with the future global crude oil trade competition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".