Bibliographic record
Abstract
China is integrated rapidly with the world economy by increasing its foreign investment linkage with other countries. In 2005 China was the 4th largest investor among emerging markets, up from 14th in 2004 with 72.4% of all economies in the world receiving Chinese FDI. Through outward FDI into any sectors, industries or regions, there should be intra-industry productivity spillovers from foreign firms to domestic firms within the same industry, mainly through reduction of production costs, technology transfer and international R& D spillovers. Diffusion channels of technology know-hows and managerial practices induced by higher FDI penetration abroad make the purpose of the increased transparency and access of core technology practical. This article addresses this question through the lens of economics as to three collaborated sets of FDI determinants, the features of FDI outflows as the overall FDI scale, the target sectors, the geographic distribution and the concrete ways of outward FDI of China. The author concludes that there should be a caveat to the non-guided outward FDI but strategically tailored to suit the requirements of multiplying the investment efficiency and climbing up the value ladder of global economy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.001 |
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".