The Impact of the Canada-Korea Free Trade Agreement as Negotiated
Bibliographic record
Abstract
This paper analyzes the impact of the Canada-Korea Free Trade Agreement on the basis of the published text and agreed schedule of commitments. We find that the Agreement reinforces existing patterns of comparative advantage between Canada (agriculture and resource-based sectors) and Korea (autos and other industries). The sensitive sectors that held up the deal for years - autos into Canada and beef into Korea - witness major trade gains, but are not unduly disrupted. In both economies, the major output gains otherwise come in non-traded services sectors, driven by income effects. We find that trade diversion effects are quite significant; this lends support for the domino theory of major free trade agreements - since the Korea-EU agreement broke the ice, the pressure has intensified on third parties to re-level playing fields by striking their own deals. The study breaks new ground in modelling services trade by developing policy impacts based on the extent to which the text of the Agreement modifies Korea's and Canada's scores on the OECD's Services Trade Restrictiveness Index and by providing estimates of Mode 3 Services trade impacts. The analysis of the Agreement as negotiated, the present study, in our view, is a step forward in understanding the impact of modern free trade agreements.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".