Local war in military-political strategy of the USA at the beginning of XXI century
Bibliographic record
Abstract
В статье анализируется новая книга, подготовленная сотрудниками Института США и Канады РАН, в которой рассматриваются вопросы, связанные с современными локальными войнами и конфликтами. В данном исследовании затронут специфический спектр политики США в начале XXI века. Автор в своей статье останавливается на наиболее крупных тематических блоках, которые связывает воедино представленную исследователями картину. В статье прежде всего акцентируется внимание на таких проблемах, как стратегия США в локальной войне в Афганистане, борьба против Исламского государства, тенденции развития американоиранских отношений, европейские проблемы. Делается вывод о том, что мощнейшая в мире военная машина оказалась недостаточно приспособленной к ведению войн нового типа, войн локальных, главными противниками в которых оказываются террористические группировки, племенные ополчения и т.п. И для победы в таких конфликтах необходимо уметь использовать различные методы борьбы. The article analyzes a new book prepared by the staff of the Institute of the USA and Canada RAS, which deals with issues related to modern local wars and conflicts. In this research a specific range of US policy at the beginning of the XXI century is affected. The author in his article dwells on the largest thematic blocks that connect together the picture presented by the researchers. The article first of all focuses on such problems as the US strategy in the local war in Afghanistan, the struggle against the Islamic State, trends in the development of AmericanIranian relations, European problems. It is concluded that the worlds most powerful military machine was not sufficiently adapted to the conduct of a new type of war, local wars, the main opponents of which are terrorist groups, tribal militias, etc. And to win in such conflicts must be able to use different methods of struggle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".