Foreign Investment Inflows to Former Socialist Countries in the Balkans: Mapping Global Capitalism
Bibliographic record
Abstract
Former socialist countries in the Balkans have attracted substantial foreign investments in their economies in the past two decades. Focusing on top-ranking investments in the region from China, India, Russia, Germany, Italy, and the Netherlands, this study examines the rationales driving these foreign capital inflows into Balkan economies, the domestic industries and assets they support and acquire in the region, and their social and political effects. Mapping these investment patterns, the essay argues, reveals the emergent dynamics of contemporary capitalism in the region and globally, especially how “central” and “peripheral” postsocialist and postcolonial economies transitioning to capitalism invest and thereby shape each other, the emerging geopolitical order of power, and the nature of global capitalism. In the Balkans these dynamics are marked by the following intersecting events: Indian, Chinese, Russian, and Balkan states transitions to capitalism after colonialism and state socialism; India, China, and Russia’s plans for power and influence in the Balkans, Europe, and globally; and desires for financial, material, cultural, and political expansion, especially among non-Anglophone European former “minor empires,” such as Germany, the Netherlands, and Italy. Foreign investment inflows into Balkan countries illuminate these important trends shaping contemporary global capitalism.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".