Editorial. Trump’s Tariff War: Impacts and Implications for World Trade and the Global Economy
Bibliographic record
Abstract
The States President Donald Trump’s aggressive tariffs regime towards world major economies have resulted in a mixed bag of economic outcomes across the regions. Due to the Trump’s announcement, the US economy Squeezed by 0.3 per cent during quarter 1 of 2025, resulting into the first decline in last three years, Similarly, China’s factory operation goes down to a 16-month low, while Taiwan’s GDP surged 5.4 per cent on pre-tariff tech exports. Europe saw 0.4 per cent growth before tariffs hit and Canada is on track to miss GDP estimates. From the US and Europe to China and Taiwan, the repercussions of his aggressive trade policies are visible in everything from factory output to GDP forecasts. The main logic given by Trump administration for the sweeping tariff policy is to restore balance to USA’s trade relations with major trading partners and protect US industries. But the reality is somewhat different. When analysed the available economic data, trends and situation revealed a far more imbalanced scenario across regions of the world. Trade experts are of then firm opinion that sweeping rise in US tariff in 2025 are impacting in a big way the world trade. Added to this, the increase in existing tariffs may distort output patterns and also may lead to a sharp reconfiguration of world value chains, resulting in a less efficient and more opaque trade system.
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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.015 | 0.025 |
| Insufficient payload (model declined to judge) | 0.013 | 0.013 |
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".