Analyses on the Pattern of Chinese FDI Utilization and Strategic Choice Based on the Competition State
Bibliographic record
Abstract
Introducing competition state model in the research of FDI utility,and by the empirical research of China's FDI utilization between 2000 and 2009.(1) Competition state is the method of effectively analyzing the trends and characteristics of FDI introduction.The method can be more comprehensive grasp the characteristics of time and space structure of the FDI destination;(2) Since the 21st century,the competition state pattern structure of China's FDI introduction is basically sound.Although the type distribution is not ideal,that's becoming more rational.The long-term FDI introduction focus on the Asia Pacific region and the United States;(3) China's FDI destination is divided into three levels in the future.The first-level position in Hong Kong,Korea,Japan,Singapore,Taiwan and Russia of Asia-Pacific region,that should take gain strategy and pioneering strategy simultaneously;The second-stage located in the United States and Canada and other developed countries of North America,that should be implemented expansion strategy;The third-level located in developed European countries,including Germany,France,Switzerland,Sweden,United Kingdom,Denmark,Italy and the Netherlands,that should be implemented selectively strategy.The African countries should adopt withdrawal strategy,unless for political reasons.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 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".