A Novel Country Classification System for Choosing International Business Locations
Bibliographic record
Abstract
This paper introduces a novel country classification system that rates the political economy risks of countries for the purpose of conducting international business. It is intended to provide investors, multinational companies, and business researchers a quick and efficient way of gauging the extent of political, economic, and legal risks associated with doing business in different countries. The study covers over 170 countries and identifies 24 country types. At the extremes are Type 1 countries (least risky) and Type 24 countries (most risky). Overall, the new classification system suggests that political economy risks associated with doing international business are relatively mild in Type 1, Type 3, and Type 4 countries. However, international businesses should temper their investment decisions with caution in Type 19, Type 20, Type 22, Type 23, and Type 24 countries due to high political, economic, and legal risks, especially Types 23 and 24 where these risks are excessive. At the same time, international businesses may want to refocus their attention to Type 11 countries who are now havens for international investments due to drastic reduction in political, economic, and legal risks associated with doing business. The twenty-four country types identified in this new classification system are time-invariant. Thus, countries may move up or down due to improvements or deteriorations in certain aspects of their political economy.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".