The Potentials of Scientific and Industrial Collaborations in the Field of REE through China’s Belt and Road Initiative
Bibliographic record
Abstract
Within the framework of trade deals and infrastructure investments, China also wants to build a "belt of scientific cooperation" with countries and international organisations involved in the Belt and Road Initiative. This could create an opportunity for involvement of several European countries that have so far treated China’s initiative with skepticism about the coherence and practicality of the project. A crucial issue that concerns both China and the European Union in the recent years is the establishment of an undisrupted supply of critical raw materials to satisfy the consumption demands of the modern high-tech world that we live in. Among the listed critical raw materials are the rare earth elements (REE). Accordingly, the development of an extended and sustainable REE supply chain is a significant research field in which both sides could collaborate and benefit from. It is crucial for the involved countries to utilise their advantages, work together and share knowledge to tackle technical, economic and environmental issues that govern the global rare earth industry. Hence, in this paper the possibilities of a potential cooperation are investigated in the context of collaborative research projects, academic networking, workshops and training for young scientists. The aim is to seek, find and bridge any gaps that exist between the two sides with a view to strong academic and industrial collaborations.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".