The Public and Environmental Aspect of Restoring Sustainable Regional Development in the Face of the Negative Impact of Military Actions on the Territory of the Country
Bibliographic record
Abstract
The main purpose of the article is to study the features of the public and environmental aspects of restoring sustainable regional development and to form an information program of the social and environmental aspects of sustainable regional development in the face of the negative impact of military actions on the territory of the country. The research methodology includes the use of a demonstration model for graphical display of the results. Taking into account expert opinions, we systematized and identified the main steps to ensure sustainable regional development in the face of the negative impact of military actions in the context of the public and environmental component. As a result, a demonstration model of ways to ensure sustainable regional development in the face of the negative impact of military actions in the context of the public and environmental component was built. The conducted study has a limitation, since Ukraine was chosen for the study, in which, at the time of writing, the aggressive military expansion by the Russian Federation continued, the model was built under the given realities of the functioning of the regional development system. In this regard, in further studies it is planned to adapt this model to the realities of other countries of the world.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".