A Crescente Presença da República Popular da China nos MENA: o caso das relações Sino-Sauditas
Bibliographic record
Abstract
Since the Bandung Conference of 1955, the People's Republic of China has established diplomatic relations with several countries in Africa, Latin America, Asia and the Middle East, thus breaking a period of diplomatic isolation and increasing its presence in other regions of the globe.Currently, Chinese involvement in the Middle East and North Africa (MENA) region is not only focused on obtaining energy resources, but also on issues of an economic, political and security nature, contemplating the geostrategic role of the region as a gateway and logistical entrepot for the Belt and Road Initiative (BRI).This increasingly consolidated presence of China in the region imposes on the MENA countries a set of opportunities and challenges that deserve an in-depth academic study.In addition to the analysis of China's historical and contemporary involvement in MENA, this paper will contemplate the analysis of Sino-Saudi relations, with the aim of understanding the implications of deepening relations between Saudi Arabia and China, as well as gauging the role that the Saudi Kingdom can play in deepening China's relations with countries in the region, and the implications of the convergence between BRI and the Saudi Vision 2030 initiative.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".