Bibliographic record
Abstract
In recent years, the world economy has become more unstable and relations between countries in different regions have deteriorated. As a result, we can talk about an international economic confrontation. The main reasons for this situation seem to be traceable: so-called trade wars, neo-protectionism, financial and other sanctions. Many countries around the world, including Russia and Spain, are victims of one or more aspects of the phenomenon. Trade wars are undoubtedly at the heart of economic battles. They have a long history, but the current wave of trade wars has been driven by President Donald Trump’s economic policies. In his opinion, the root of the U.S. economic problems lies in its trade deficit with China, the European Union, Mexico and some other countries of the world. With the idea that “trade wars are good and easy to win”, Washington unilaterally tore up the agreements reached: the Paris climate accord, the Trans-Pacific Partnership, the nuclear deal with Iran, the free trade agreement with Mexico and Canada. In addition, Trump has raised tariffs on hundreds of manufactured goods imported by the United States (steel, aluminum, washing machines, solar panels, etc.) and threatened the European Union with higher tariffs on cars. The trade wars unleashed by the White House will have long-term direct and indirect consequences for the state of the world economy. Russia and Spain are heavily dependent on international markets. That is why complications in world trade are contrary to the interests of both countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".