Bibliographic record
Abstract
Canada’s participation in the Trans-Pacific Partnership (TPP) negotiations is an opportunity to advance Canada’s commercial interests in the Asia-Pacific region. The twelve countries in the TPP Agreement would form one of the largest trade areas in the world with trade among TPP member countries of US$4.0 trillion and trade between TPP countries and the rest of the world of US$5.4 trillion. The TPP countries as a group would be by far Canada’s largest trading partner. Trade with other TPP members accounted for 81.1% of Canada’s total exports to the world and 65.9% of total Canadian imports from the world. This study assesses the economic impact on Canada and other TPP members including both developed and developing members based on the final negotiated outcomes of the TPP agreement that was concluded in Atlanta, Georgia, U.S.A. in October 2015. The economic impact assessment of the TPP was based on simulations with a computable general equilibrium (CGE) model. The CGE model used for this analysis is the Global Trade Analysis Project (GTAP) model with the GTAP database version 9 that is provided and supported by Purdue University, U.S. . This model and other versions of the GTAP model family are used widely by many governments, academics and research institutes around the world to conduct assessments of potential economic impacts of their trade liberalization initiatives.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".