Declining Risk, Market Liberalization and State-Multinational Bargaining: Japanese Automobile Investments in India, Indonesia and Malaysia
Bibliographic record
Abstract
corporations (MNCs) , by their organizational structure and operations, have profoundly shaped the world economy over the past five decades. As noted by Raymond Vernon, prior to World War II terms such as multinational or transnational were seldom used to describe international economic relations. However, by the mid-1990s, the aggregate sales of the subsidiaries of all multinational corporations exceeded total world exports.1 Paralleling the MNCs are nation-states that are the principal agents responsible for the organization of political and social life in a given society. The mutual interplay of the two has been a central concern that has occupied the attention of theoreticians as well as policy-makers.2 Analysts often observe that host countries can offer foreign investors access to new markets, resources, and factor endowments endemic to each society. On the other hand, multinational enterprises possess enormous capital, managerial expertise and innovative technology that can contribute to the developmental goals of a country. The expansion of multinational activity across borders and host country policies designed to attract, screen and control prospective investors invariably entails a process of bargaining and negotiation. In the state-MNC literature, most theoreticians have analyzed bargaining power by looking at the resources or unique characteristics of the actors. Dependency writers have historically argued that MNCs are the organizational embodiment of international capital whose operations undermine the developmental goals of local economies.3 According to this line of reasoning,
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".