China and the Trans‐Pacific Partnership: A Numerical Simulation Assessment of the Effects Involved
Bibliographic record
Abstract
Abstract The Trans‐Pacific Partnership (TPP) is a new negotiation on cross‐border liberalisation of goods and service flows going beyond WTO disciplines and focused on issues such as regulation and border controls. This paper uses numerical simulation methods to assess the potential effects of a TPP agreement on China and also China's inclusion or exclusion on other countries. We use a numerical 11‐country global general equilibrium model with trade costs and inside money. Trade costs are calculated using a method based on gravity equations. TPP barriers potentially removable are trade costs less tariffs. Simulation results reveal that China will be slightly hurt by TPP initiatives in welfare when China is out, but the total production and export will be increased. Other non‐TPP countries will be mostly hurt in welfare, but member countries will mostly gain. If China takes part in TPP, she will significantly gain and increase other TPP countries' gain as well. The comparison of TPP effects and global free trade effects show that the positive effects of global free trade are stronger than TPP effects. Japan's joining TPP would be beneficial to both herself and most of other TPP countries, but which negative effects on China's welfare when out of TPP will increase further.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".