SOCIAL, ECONOMIC, AND ENVIRONMENTAL IMPACTS OF THE ONE BELT ONE ROAD INITIATIVES
Bibliographic record
Abstract
The One Belt One Road Initiative (OBOR) by China presents a grand vision to the world that aims to foster cooperation among different countries in various fields such as global trade, international relations, infrastructure development, education, and technology. Also known as the Belt and Road Initiative (BRI), it comprises a network of roads, railways, and sea routes, all geared toward the development of humanity. The purpose of this research is to analyze some of the significant impacts of this massive project in terms of social, economic, and environmental aspects. Through the exchange of culture, sports, education, and international relations on six economic corridors, the BRI has the potential to create substantial economic benefits. The project also prioritizes environmental sustainability through a green BRI approach. All the quantitative and qualitative data are extracted from different research papers/reports, published books, and some online based data portals. It has been shown that the OBOR/BRI seeks to connect the world and foster peace, whilst its implementation may face significant challenges. Nonetheless, it presents new opportunities for people and may usher in a new era of globalization.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".