Emergency Management in Resolving an Emergency Situation
Bibliographic record
Abstract
Experience gained from NATO operations shows that the basis for an effective solution to a crisis is a combination of a comprehensive political, civilian and military approach. The cooperation of all stakeholders is thus a basic prerequisite for the effective resolution of crisis situations. These aspects and stakeholders include emergency management. This paper deals with civil-military cooperation in times of emergency caused by the COVID-19 pandemic in the Czech Republic. It qualitatively evaluates the findings resulting from the questionnaire survey focused on the state of crisis preparedness of the Army of the Czech Republic and the functionality of emergency management in cooperation with rescue work with other teams of the rescue system. The questionnaire was carried out at military units in all regions of the Czech Republic; organizational units of the Ministry of Defence with nationwide competence, which were directly involved in securing measures related to the declaration of a state of emergency due to the COVID-19 pandemic in March—May, 2020; Operations Command, which currently manages operations in the Czech Republic designed to manage the consequences of a pandemic; and members of the Ministry of Defence participating in the activities of the Strategic Command and Control Group. A total of 21 stakeholders took part. The experience in managing the consequences of the COVID-19 pandemic have shown that armed forces around the world have an irreplaceable position in dealing with nonmilitary crises. The conclusions and recommendations obtained from the research survey are the content of this paper.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".