Three Wrongs Do Not Make a Right: The Conundrum of the US Steel and Aluminum Tariffs
Bibliographic record
Abstract
Abstract In March 2018, the United States enacted tariff increases on a vast range of imported steel and aluminum products. The Trump administration cited national security concerns as the justification, claiming an exception under GATT Article XXI. In response to these tariffs, several WTO Members, including the European Union, Canada, Mexico, China, Russia, and Turkey, adopted their own tariffs against imports from the United States, justifying their tariffs under the WTO Agreement on Safeguards. Other Members, such as South Korea, Brazil, and Argentina opted for quota agreements on these exports with the United States in exchange for exemption from the tariffs. This article argues that none of these measures is consistent with WTO rules. The sweeping tariffs that the United States have adopted, the retaliatory measures that several Members have implemented, and the bilateral quota agreements that three Members concluded with the United States are indeed ‘three wrongs’ that do not make a right, but rather endanger the stability of the international trading system under WTO legal disciplines.
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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".