China's outward investment activity: Ambiguous findings in the literature and empirical trends in greenfield investments
Bibliographic record
Abstract
Abstract With the rapid increase of China's outward foreign direct investments (OFDIs) since the early‐2000s, a growing body of literature has developed that investigates investment processes and their underlying motivations and tendencies. Three important findings emerge from this literature. First, it has been noted that the generation of market and resource access have been key drivers of investment activity. Second, China's OFDIs have accordingly focused on mature manufacturing and natural resource sectors. Third, a large proportion of OFDIs is assumed to have been directed to neighboring countries in East Asia or other developing economies, for instance in Africa. However, a literature review reveals limitations in prior studies with respect to measurement biases, database incompatibilities, the neglect of a knowledge perspective, and a lack of sectoral differentiation. Descriptive analysis based on a comprehensive firm‐level data set of greenfield investments shows that previous findings are only partial. According to fDi Markets data from 2003 to 2016, OFDIs from China are more diversified and widespread than assumed. Many recent investments have a distinct knowledge motivation, are focused on high‐tech and business service sectors and non‐manufacturing functions, and are directed toward developed economies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".