Bibliographic record
Abstract
The study examines the impact of D. Trump's new tariff policy on US agri-food exports, aiming to identify the consequences of protectionist measures for the American agricultural sector and farmers. The methodology is based on contextual and logical analysis, comparison and generalization methods. The research establishes that the US agricultural sector becomes the primary target of retaliatory trade measures, leading to export reduction and decreasing farm incomes. Federal economic assistance programs only partially compensate losses, providing farmers with support amounting to 10 billion dollars under the Farm Bill extended until September 2025. The analysis shows that traditional importers of American products are reorienting toward alternative markets: China, having imposed tariffs of 135 to 145% on American agricultural products, increases purchases from Brazil, whose soybean harvest in 2025 will reach a record 164.3 million tons, with total production of grains, legumes, and oilseeds reaching 327.6 million tons. The study reveals specific features of US trade relations with Canada, where high tariffs on dairy products reach up to 300%, and with Mexico, which remains the largest export market for American agricultural products. For the EU, increased duties of up to 200% on European wines threaten the loss of the American market, which accounts for a quarter of wine exports from Italy and Spain. According to the WTO, the imposed tariffs will lead to a 0.2% decline in international trade in 2025 and an 8-10% increase in global food prices. The study concludes that Russian exporters will not gain significant advantages from the trade confrontation, as Russia is not considered by China as a key alternative supplier of agricultural products.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".