Bibliographic record
Abstract
A trade war between China and us has started in 2018. China has been considered a threat by American politicians they are trying to use a trade war as a tool to contain China’s high-speed development. However, China will not allow the US to be seized without putting up a fight. With the advance of the trade war, China and the US have had several rounds of confrontation by raising tariffs. It’s hard to really say which side won the overall victory, nevertheless, the trade war between China and the US has diffused the whole world economic environment. In order to avoid the raised tariffs from the competitor, China, and the US transfer the import resources to the country that has lower tariffs on the same goods. The third-party country would attend the trade war by gaining the spillover effect of the trade war. Southeast Asia has eleven countries, such as Thailand, Cambodia, Vietnam, the Philippines, Malaysia, Singapore, Indonesia, and Timor Leste. Most of them have China or the US as their biggest trading partner. When China raises tariffs on a certain commodity to the United States, which may find the third country in order to replace China to evade tariff sanctions. The Countries in the Southeast become the optimal choice for the US. They also have low labor costs, close transportation routes, and many employment vacancies. Therefore, Southeast Asia has become one region in the world that has a spillover effect of the China-US trade war.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".