Усиление протекционизма во внешней торговле США (Increased protectionism in US trade policy)
Bibliographic record
Abstract
Russian Abstract: Авторы рассматривают изменения, произошедшие во внешнеэкономической политике американской администрации после избрания Президентом Д. Трампа. Главный смысл изменений заключается в пересмотре международных соглашений США с североамериканскими торговыми партнерами (Канадой и Мексикой), установление высоких пошлин на импорт товаров из Китая. Д. Трамп обосновывает новую политику отказом от глобализации и переходом к использованию концепции экономического патриотизма, тесно связанной с теорией экономического национализма и тем самым де факто разваливает объективно сложившиеся в мировой торговле глобальные производственные цепочки. Дается перечень основных торговых войн, которые проводили США в предыдущие годы и в заключении делается вывод о бессмысленности поиска решения экономических проблем через торговые войны. English Abstract: The authors consider the changes in the US international economic policy after D. Trump won the presidential election. The major changes involve revising US international agreements with North American trading partners (Canada and Mexico), imposing higher import tariffs on goods from China, refusal to conclude the Trans-Pacific Partnership (TPP). D. Trump justifies the new policy by the rejection of globalization and the transition to the concept of economic patriotism, which is closely related to the theory of economic nationalism. The main trade wars conducted by the United States in previous years are listed and it is concluded that addressing economic issues through trade wars is not reasonable. Note: Downloadable document is in Russian.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".