Assessing the Impact of China-Canada FTA with G-Dyn Model
Bibliographic record
Abstract
General background: Trade and investment between China and Canada has long been prosperous in many industries like natural resource, agricultural products, textile, machine and equipment manufacturing, real estates and high-tech industries. China has been the second largest trade partner of Canada for over decade, while Canada is also a major destination of China’s goods and services export, investment, and also labor immigration. Discussions on China-Canada Free Trade Agreement (CCFTA) have been existing for long and are getting especially intensive at this moment when the U.S. just started its process of renegotiating NAFTA (North American FTA) and Canada needs to find its new way of cooperation, a rapidly growing and opening China will be a considerable option. Methodology: G-Dyn model extends the standard static GTAP model to include capital accumulation, adaptive expectation theory of investment and international capital mobility. Basing on G-Dyn, we implement sticky/flexible wage mechanism originated by Dixon and Rimmer(2002), an USAGE-type investment theory considering the risk-averse behavior of investors to provides more flexibility for modeling investor’s expectation and improves G-Dyn model robustness when constructing baselines or performing policy simulations. Simulations: (1) Tariff of good trade is largely cut between China and Canada; (2) Non-tariff barriers for goods are reduced bilaterally; (3) Tariff equivalent is reduced on service trade; (4) Barriers are partly removed in Foreign Direct Investment; (5) Trade facilitation gets improved.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".