Structures of globalization: Evidence from the worldwide network of bilateral investment treaties (1959–2009)
Bibliographic record
Abstract
Existing sociological theories of international relations yield contrasting predictions for their structure that range from an increasingly dense and universally tied network to networks in which ties tend to concentrate within or between certain types of economically, politically or culturally defined blocs. We contribute to this growing body of empirical research by analyzing original data on the complete worldwide network of bilateral investment treaties (BITs) as it has evolved over the course of 50 years since its inception in 1959. We find that the number of BITs increases almost exponentially over time to include nearly all of the world’s countries. However, the density of ties between advanced capitalist and others is stronger than for any other dyadic types. We also find patterns of regional homophily, but only in Asia, East Asia, Postsocialist Europe and Eurasia, and North Africa/Middle East. These findings suggest that existing explanations of globalization are more complementary than contending. Theorizing about any particular global outcome thus requires attention to the simultaneity of material and cultural forces and the interplay of transnational and local socio-historical developments. Our analysis of the structure of the BIT network also helps explain the weak link between foreign direct investment and BITs discovered in previous research.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".