Is Using Trade Policy for Foreign Policy a ‘SNO Job’? On Linkage, Friend-Shoring, and the Challenges for Multilateralism
Bibliographic record
Abstract
Abstract Using trade policy to achieve foreign policy objectives, such as stable international relations, has a long history, from Kant to the founders of the GATT. Punishing enemies and rewarding ‘friends’ by granting or withholding market access is also not new, and sanctions or blockades are a venerable form of trade policy used as foreign policy. A more recent form is influencing the domestic policy of another country with non-commercial provisions in trade agreements. All these tools are based on linkage, on the assumption that a desired outcome can be achieved by interventions that would increase or decrease trade. The latest instance is so-called ‘friend-shoring’, which would in principle isolate enemies, although it will be difficult in practice and risks undermining multilateralism. The cost of these interventions is susceptible to economic analysis, even if the conclusion is that it is worth paying. Influenced by Alan Winters who referred to national security as a motivation for agriculture protection as a ‘so-called non-economic objective’ or SNO, I argue that using a trade policy tool for a foreign policy purpose as if there is no cost is a SNO job, an attempt to justify an intervention aimed at one objective by framing it as being valuable for another.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".