Social and Political Development Strategies: Global Pandemic Challenges (Covid-19)
Bibliographic record
Abstract
Even English historian A. Toynbee claimed that each new generation of the “creative minority” (elite) periodically faced challenges of the time, to which it had to seek and give an adequate response. In case of an unsatisfactory answer, this “creative minority” should leave the historical stage, giving way to the more competent elite. The coronavirus crisis experienced by the global community in 2020 has become such a global challenge of our time for many people. And the public can make conclusions about the professional training and level of competence of the ruling elite groups judging from how effectively they cope with this challenge. But even now it can already be stated that the crisis has revealed a number of significant systemic failures in the functioning of political elites - their slow reaction to the event, disunity, clip consciousness and underestimation of the degree of risk, resources and their own capabilities. This article is devoted to the analysis of all these problems.
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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 | 0.002 | 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 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| 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".