Bibliographic record
Abstract
Background: Geopolitical risk has become a first-order determinant of internationalization decisions, shaping both where firms expand and how quickly they retreat. This review synthesizes research on geopolitical risk, sanctions exposure, and macro-financial tightening and connects it to market selection and exit choices. Methods: We develop a structured review anchored in decision theory, real options logic, and international business research. Prior findings are organized into a process model spanning scanning and entry, escalation management, and exit governance.Results: : The synthesis identifies three recurrent mechanisms: (i) risk repricing through capital flows, currency volatility, and financing conditions; (ii) operational disruption via trade controls, cross-border payments friction, and compliance costs; and (iii) strategic lock-in created by asset specificity, network dependence, and institutional embeddedness. We propose a market selection and exit matrix and a set of testable propositions linking risk signals to entry mode, pacing, and exit timing.Conclusions: : Internationalization under geopolitical risk is best understood as a dynamic portfolio problem. Resilience depends on optionality, diversified financial and operational channels, and disciplined exit governance that preserves re-entry pathways while limiting non-linear exposure.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| 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".