Bibliographic record
Abstract
Could we measure the impact of geopolitical shocks, such as the Russian invasion of Ukraine and COVID19. on shaping economic diplomacy and investor decisions? This is the question that runs through this study. We are attempting to highlight and identify the vital interconnections between the economy as reflected in the functioning of a state's stock market and the geopolitical risks it faces, attempting to draw useful conclusions for economic diplomacy. The economic analysis of geopolitical risks and its integration into the economic policy framework is a major concern of the current scientific research and therein lies the value of the present study. By statistically exploiting data from 2000 for the countries facing strong geopolitical risks: the BRICS (Brazil, Russia, India, China & South Africa), MENA (Middle East & North Africa countries: Algeria, Bahrain, Djibouti, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Palestinian Authority, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates & Yemen) and SAHEL (four countries bordering Lake Chad - Cameroon, Chad, Niger, Nigeria – as well as Burkina Faso, The Gambia, Guinea, Mali, Mauritania, and Senegal), but also for the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, & the US, as well as the European Union) due to their involvement in geopolitical crises (either through peacekeeping missions and development aid or by sending military equipment), it is possible to analyse the effects of geopolitical risks on the functioning of their economies, as reflected in stock market fluctuations. The conclusions are twofold: they feed the information "arsenal" for shaping economic diplomacy, and at the same time help investors to protect themselves from geopolitical risks by diversifying their investment portfolio.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".