Subsidizing the Microchip Race: The Expanding Use of National Security Arguments in International Trade
Bibliographic record
Abstract
In 2018, China, India, the European Union, Canada, Mexico, Norway, Russia, Switzerland, and Turkey lodged complaints with the World Trade Organization’s (WTO) Dispute Settlement Body (DSB) in the case of Certain Measures on Steel and Aluminium Products. Each State alleged that the United States had violated international trade law by imposing a series of aggressive tariffs on steel and aluminum imports. President Donald Trump’s administration responded to these allegations by claiming that its actions were permissible under Article XXI of the General Agreement on Tariffs and Trade (GATT); a long-standing exception built into the international trade law framework that allows States to restrict trade when doing so is vital to their national security. According to the United States, Article XXI granted it and other States near-total discretion when it came to defining their own security interests. The DSB Panel rejected this argument. Instead, the Panel asserted its own power to determine whether a State could claim the protection of Article XXI. When it came to American tariffs on imported metals, the Panel found that Article XXI was not appropriately implicated.
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 | 0.037 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.012 | 0.012 |
| 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".