The Effects of China’s “Belt and Road Initiative” and “31 Measures to Benefit Taiwan” on the Development of Taiwanese Enterprises
Bibliographic record
Abstract
China has achieved rapid economic growth and become involved in the economic globalization through its policy of reform and opening-up and modernization. It has attracted much investment from lots of Taiwanese enterprises, including some small and medium-sized enterprises featuring a high labor cost and facing difficult operation in the traditional industries. Thanks to the policy, many Taiwanese enterprises have got a chance to rebirth by transforming their crises into opportunities. With the implementation of the policy of urbanization, the people from rural areas in China have been moving to urban areas, and the enterprises of the second and third industries have been concentrating in cities. This has not only fueled the livelihood-oriented consumption in China but also expanded the domestic demand market of the Taiwanese medium and large-sized livelihood enterprises in China. The Belt and Road trade foundation construction program, which aims to link Europe, Asia and Africa and was proposed in 2013, is an extension of the Great Development of Western Part of China and offers Taiwanese enterprises a chance to get fully involved in the development of the international market. The 31 Measures to Benefit Taiwan announced by the Chinese government in February 2018 has significant influence on the future development of the Taiwanese enterprises in China. Therefore, this paper will elaborate on the effects of the Belt and Road and the 31 Measures to Benefit Taiwan on the Taiwanese enterprises.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".