Analyzing the Characteristics and Evolution of Chinese Enterprises’ Outward Forward Direct Investment Host Country Network
Bibliographic record
Abstract
With the promulgation of the “Going Global” strategy, Chinese enterprises’ outward forward direct investment (OFDI) moved towards a stage of rapid development. Based on the data concerning projects of Chinese enterprises, this paper establishes a network of OFDI host countries with the industry as the media and analyzes the network and its evolution characteristics by using the complex network method. Results show that the number of network nodes is increasing, and the edges between communities are greater, and the number of countries along the Belt and Road route is increasing. The network is a so-called small-world network. To reduce investment risks, it is necessary to identify the key nodes in the network. Based on the centrality of key nodes in complex networks, the improved “scatter degree” method is used to evaluate the importance of each host node. Results demonstrate that the United States and Australia are the most important countries for Chinese enterprises to invest overseas, followed by Brazil, Canada, Russia, Indonesia, and the United Kingdom (UK). Excluding the UK, all other countries are resource-rich. Thus, it can be seen that the FDI of Chinese enterprises is mainly resource-based investment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".