Bibliographic record
Abstract
FDI behavior has been receiving growing attention in the world. FDI may be viewed as a typical spatial interaction process, which is not only determined by the attributes of origin (home) and destination (host), but also confined by spatial separation configurations. The origin and destination attributes are known as comparative advantages factors, location endowments, ownership endowment, and the internalization of multinational corporations. So the distribution of FDI should be analyzed in a comprehensive context. The changing distribution of U.S. FDI by regions and industries indicates that the spatial structure of U.S. FDI abroad, especially in Europe, Canada, Latin American, and Asia and Pacific Region, is relatively steady than that of the industries, which is transformed from the concentration on manufacture industries to finance, insurance and estate industries within the past two decades. Since FDI plays and a significant role in regional development, more efforts should be made by Chinese government to absorb much foreign investment, and on the other, to enlarge investment abroad, so as to promote China's economy to a new stage. Meanwhile, emphasis should be rested both upon the construction of networks between MNCs and local firms, and learning and innovative abilities of regions and firms should be strengthened simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".