US tariffs will test trade alliances and architecture
Bibliographic record
Abstract
Significance The USTR will subject 284 more products worth 16 billion dollars to public comment. President Donald Trump is intensifying his efforts to return manufacturing to the United States by imposing steep tariffs on industrial imports from leading trading partners including China, the EU, Canada, Mexico and Japan. China’s finance ministry announced it would impose matching tariffs on 545 categories of US imports from the same date. Tit-for-tat retaliation and belligerent rhetoric are exacerbating concerns about the broader challenge to the global trade regime. Impacts North American Free Trade Agreement (NAFTA) renegotiations are unlikely to conclude in 2018, increasing the chance of US withdrawal. Trade splits with allies will damage Washington's ability to coordinate efforts to confront China on unfair intellectual property transfers. Globally integrated manufacturing supply chains and financial markes will cause US-China trade actions to impact firms in many countries. Cross-border investment fell by 23% in 2017 according to the United Nations -- another fall is likely this year due to the trade conflict.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.112 | 0.022 |
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".