The impact of Trans-Pacific Partnership agreement on the Canadian economy
Bibliographic record
Abstract
Abstract The Trans-Pacific Partnership is the most comprehensive trade agreement in the world. The TPP will help deepen Canada’s trade ties in the dynamic and fast growing Asia-Pacific region while strengthening existing economic partnerships with NAFTA partners and across Americas. The TPP will eliminate tariffs on almost all of Canada’s key exports and offer access to new opportunities in the Asia-Pacific region. Tariffs and other barriers on a wide range of Canadian products from various sectors will be reduced, including in agriculture and agri-food, fish and seafood, forestry and wood products, metals and mining and industrial goods. These benefits can only be derived if USA ratifies it. However, the US president has already signed a presidential memorandum confirming the US withdrawal from the TPP agreement. With this background, the current study evaluates the economic impacts of the Trans-Pacific Partnership agreements on the Canadian economy by the year 2030 using a global CGE framework. The study undertakes a number of simulations based on the level of tariff reduction across selected commodities between Canada and other TPP Nations. The GTAP 9 Data Base with the reference year of 2011 is used for the study. Results show that Canada stands to benefit significantly from improved access to the TPP region. Canada expects a considerable increase in agricultural export. Canola, processed food and beverages, seafood, beef and pork sectors are expected to benefit from the deal. Industrial goods like farming and construction equipment, metal and mineral, transport equipment, machinery would gain from TPP agreements. The agreement would help increase Canada’s manufacturing and exporting output. The banking sector is also expected to benefit from the deal. Additionally, a significant number of skilled and unskilled employment is likely to generate in Canada.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".