Bibliographic record
Abstract
Abstract During the Covid‐19 pandemic, China's Belt and Road Initiative (BRI) projects in the Middle East first struggled but soon stabilized. This article studies the why and how by examining the cases of Iraq and Syria to observe the ways China handled its international business operations in the time of Covid. Prior to that, despite international criticism and doubts, China's BRI had continued to thrive. In the Middle East and North Africa, China had been forming partnerships under BRI with many countries and sought to connect with national development plans such as Saudi Arabia's Vision 2030, Kuwait's Vision 2035, and Qatar's Vision 2030. After the Covid‐19 outbreak in December 2019, in anticipation of a resultant global economic recession, China's economy experienced a 6.8 percent contraction in the first quarter of 2020. Recently, China reported 2.3 percent overall GDP growth in 2020 and an 18.3 percent growth spurt in the first quarter of 2021. These developments prompt one to ask, what impact has Covid‐19 had—or what effects will it have—on China's BRI projects in the Middle East? To search for an answer, this study zooms in to two of the hardest‐hit Arab countries: Iraq and Syria. Both represent an investment environment entirely different from those of the affluent Gulf Arab states. Throughout Covid‐19, Iraq and Syria have been facing insufficient public‐health facilities and a lack of medical equipment, on top of political instability and economic challenges. This study looks at how China managed its BRI operations in Iraq and Syria during this crisis.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| 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".