Bibliographic record
Abstract
The Rating of Global Risks for Eurasia in 2019 is the first analytical project to assess global risks for the Eurasian macroregion. The risk rating was released as part of the Astana Club international meeting and is based on a survey of over 1,000 professional respondents from 60 countries and detailed opinions of 30 prominent international experts. The world’s vulnerability to global risks will grow – the overwhelming majority of experts who participated in the project are sure of this. Pessimism seems to be associated with a new round of tension in the Middle East, crises around Iran and Ukraine, rising tensions in the South China Sea, and sanctions against Russia. Another trend that will continue in the coming year is the rise of protectionism. 56 percent of polled experts are inclined to this opinion. Pessimism is added by the aggravation of trade conflicts between the United States and its key trading partners: China, the European Union, as well as the NAFTA members such as Canada and Mexico. Although part of the conflict has been resolved, the confrontation between the United States and China will not lose its relevance in 2019. The global geopolitical and geo-economic confrontation has been spilling over onto the new area: the cyber environment. World tensions have also kept the countries from finding common ground on solving environmental problems and challenges.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".