Is the European Union Trade Deal with Canada New or Recycled? A Text‐as‐data Approach
Bibliographic record
Abstract
Abstract Are the rules in the Canada‐European Union Comprehensive Economic and Trade Agreement (CETA) largely copied from past trade agreements, or are they new and potentially groundbreaking? Some critics charge that CETA merely replicates the failures of past trade deals, while others worry that CETA is specifying new ‘behind the border’ rules that threaten state sovereignty. Using text analysis we compare the contents in CETA to those in previous trade agreements signed by both parties. Unlike many other recent trade deals, we find that much of the content in CETA is indeed novel. On average only about 7 per cent of CETA language is copied directly from any of the 49 previous trade agreements we analyze. This same pattern holds across many of the most controversial issue areas, like investment. Some recent agreements like EU‐Singapore (30%) and Canada‐South Korea (24%) are replicated in part in CETA, although recycled text is more likely to come from past Canadian PTAs than EU ones. Our results suggest that fears that CETA is ‘more of the same’ are overblown and indicate that if ratified CETA likely will play an important role as a model in future trade agreements.
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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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.033 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".