Do free trade agreements promote sneaky protectionism? A classical liberal perspective
Bibliographic record
Abstract
Abstract A neglected aspect of regional trade agreements (RTAs) is their protectionist potential. In times of a stagnating World Trade Organization (WTO), growing economic nationalism and skepticism about the merits of free trade and trade agreements, the paper examines to what extent recently signed RTAs really promote genuine free trade or rather foster sneaky protectionism under the guise of free trade. For this, the paper proposes an ideal-type free trade agreement benchmark model based on a classical liberal perspective and applies it in a multiple case study approach to assess three cases of recently concluded mega-RTAs: the Comprehensive and Progressive Trans-Pacific Partnership (CPTPP), the renegotiated North American trade agreement USCMA, and the Canada–European Union (EU) agreement CETA. The article shows that all of them are far from the classical liberal ideal of totally free trade and have a high content of back door protectionism suitable to raise trade barriers when politically opportune. In particular, the United States–Mexico–Canada Agreement (USMCA) includes many clear protectionist provisions that might even outweigh its liberalizing stipulations, whereas CPTPP and CETA can be deemed net liberalizing. It concludes that given political economy constraints, RTAs can nevertheless remain a second-best solution to the classical liberal ideals of completely unhampered trade and unilateral liberalization provided that they remove more impediments to free exchange than they cement or create.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".