Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 it