The energy factor of the Persian Gulf countries in the American strategy of containing the PRC
Bibliographic record
Abstract
The article examines the foundations of the US geopolitical interests in the Persian Gulf region, the increasing influence of China on the region and the subsequent US strategy regarding the containment of the PRC. The Persian Gulf region is of economic interest to a number of countries due to its rich hydrocarbon resources, as well as its unique geographical location, which has historically been subject to geopolitical influence. The stable growth of China's economy (before a 6.8% decline in GDP during the pandemic in the first quarter of 2020), China's growing cooperation with the Persian Gulf countries causes the United States to fear about losing its weight in world politics and, in particular, the loss of influence in the Persian Gulf region. В The article shows the main directions and ways of implementing the American strategy of containing China in the context of the energy factor of the Persian Gulf countries by: creating a "Middle East Strategic Alliance" (MESA), increasing the share of energy exports to the Chinese market for use later as a tool of pressure on China. Special attention is paid to the possibility of the United States joining the "price war" to restore oil prices. The study revealed that the energy factor of the Persian Gulf countries plays a significant role in the American strategy of deterring the PRC, which is determined by a number of geopolitical, economic and other factors.
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 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 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".