Application of Anti-Crisis Measures for the Sustainable Development of the Regional Economy in the Context of Doing Local Business in a Post-COVID Environment
Bibliographic record
Abstract
The main purpose of the article is to form a theoretical and methodological mechanism for the application of anti-crisis measures for the sustainable development of the regional economy in the post-COVID environment. For this purpose, it was chosen to separately take the region as an example of the application of the proposed mechanism. The research methodology involves the use of methods for graphical modeling of the mechanisms for making and implementing managerial decisions. The popularity and convenience of the methods make it possible to effectively form the desired mechanisms. The relevance of the topic is due to the fact that the world has finally moved into the post-COVID period of development. COVID-19 has not gone away, but the regional economy must move forward. As a result of the study, a theoretical and methodological version of the possibilities of applying anti-crisis measures for the sustainable development of the regional economy in the post-COVID environment was presented. The study has a number of limitations, and they relate to the lack of opportunities to apply the proposed mechanism to a larger number of regions. Further research should cover more regions and take into account not only the post-COVID conditions of sustainable development but also the Ukrainian-Russian military crisis.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".