Managing Geopolitical Risks
Bibliographic record
Abstract
Geopolitical risk has emerged as an important factor in foreign investment decisions in recent years. The rise of geopolitical tensions worldwide and the fragmentation of relationships between countries have introduced new dimensions to foreign investment risks. MNEs and their managers are taking notice. Many have established dedicated roles to address geopolitical risk. For example, in 2023, the Financial Times reported that Goldman Sachs had launched a Global Institute to advise clients on geopolitical matters due to heightened demand. Similarly, Lazard established a geopolitical risk unit to “capitalize on global volatility.” The symposium will open with introductory keynote comments by two prominent scholars in the field, it will continue with four paper presentations, and it will conclude with a brief question-and-answer session with the audience. The ensuing discussion is envisioned to help advance both conceptual and empirical work on the management of geopolitical risk. We hope to also shed light on promising areas for further research that leverage both novel approaches to theory and explore new empirical settings. CORPORATE DIPLOMACY AND EXIT STRATEGIES Author: Mike W. Peng; U. of Texas at Dallas STRATEGIC RIVALRY, TRADE DEPENDENCE, AND FOREIGN SUBSIDIARY INVESTMENT Author: Flladina Zilja; Copenhagen Business School
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".