Chinese stategic [sic] (outward) investment in Latin America: case study - Brazil
Bibliographic record
Abstract
Chinese remarkable economic growth averaging at 10 per cent per annum since the late 70's has been facilitated by a strategy of promoting exports and attracting foreign capital. Multinational companies from the US, Japan, and Europe have led their way in using China as off-shoring platform for the labor-intensive stages of goods production. China currently has an account surplus of 6-7 per cent of their GDP and has accumulated more reserves than any country in the world at approximately US$ 300 trillion. Given its standing as one of the major suppliers of global capital, Chinese government in the recent years has been pursuing a policy of outward FDI under which some of the state-owned Chinese enterprises have been provided with soft capital to become global leaders on the lines of Japanese and Korean trading houses. The financial crisis of 2007/08 had enabled the Chinese government an opportunity to provide capital to companies in the developed and developing world who are currently starved for financing. The analytical framework of internationalization theory suggests that one should look into the motivations for internationalization and the literatures identified the following drivers of internationalization process: (a) Market-seeking FDI, (b) Resource-seeking FDI, (c) Efficiency seeking FDI and (d) Strategic asset-seeking FDI. Using the case study of PETROBRAS and MMX Mining in Brazil, the study found that major driver of investment by Chinese companies is in acquisition of strategic assets for China's future development. In the case of PETROBRAS, the mode of investment was supplier's credit rather than the traditional FDI or portfolio investment. In the case of MMX, it was the stake in the equity capital of the firm. Thus, the modern modes of outward FDI by Chinese firms are more roundabout forms rather than the traditional modes of Foreign Direct Investment and Foreign Portfolio Investments addressed by most researchers. These investments have blurred the traditional distinction between investments (in
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".