MOBILIZATION MECHANISMS OF STATES IN THE POLICY OF COUNTERING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
In the course of a round table with international participation organized by the Department of Comparative Political Science of Lomonosov Moscow State University and the electronic journal “Bulletin of Moscow State Regional University”, a discussion was held on the mobilization mechanisms of states (Russia, China, India, Canada, Great Britain, Kazakhstan, etc.) in countering the COVID-19 pandemic. The main topics of discussion include the following: state mobilization mechanisms in the situation of the first and subsequent waves of the COVID-19 pandemic, interaction between citizens and authorities, the perception of the population of anti-epidemic measures of governments, changes occurring under the influence of the pandemic in the social, economic, political spheres of society, in the areas of digitalization and education, forecasting the progress of the fight against the pandemic. The experts at the round table were political scientists of the Lomonosov Moscow State University, National Autonomous University of Mexico (Mexico City, Mexico), Al-Farabi Kazakh National University (Almaty, Kazakhstan), universities of the People’s Republic of China.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".