China Inward FDI and Chinese Exports to High-Income Countries (HICs): A Historical Perspective Based on Bibliometric Method
Bibliographic record
Abstract
PR China is capturing FDI mainly from the overseas Chinese territories and from High-Income Countries (HICs); this last group is also the most important Chinese export-market. Adopting an inductive methodology, based on the bibliometric method, our purpose is the understanding of which were the determinants that led to attract inward FDI and, later, to increase exports from PR China to HICs. Taking into account the time frame 1980–2010, 367 different publications were collected. Using the ISI Web of Science and the HistCite Software 10 research papers were the basis for a bibliometric diagram (LCS > or equal to 5). From this diagram was possible to extract two main streams: economic reforms toward an inward FDI attraction and world exports leadership; and inward FDI—mainly from HICs—led to an increasing value of the exports basket. A third issue emerged also, not as a stream, but as an important conclusion based on the Gilboy (2004) paper, published on Foreign Affairs: foreign companies dominate high-tech industries and a lion’s part of Chinese industrial exports. PR China has reached the world exporting ranking leadership based on economic reforms, focused on the attraction of FDI export driven, which had on companies from HICs a major contribution.
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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 | 0.054 | 0.076 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".