Reducing the Risk of Disasters Caused by Epidemics
Bibliographic record
Abstract
Epidemics are the most common natural phenomena that have occurred throughout the entire history of human society. Depending on the type of disease and the development of the collective immunity that society had acquired by then, the consequences of epidemics were usually very severe. Precisely because of this, the aim of the paper is a scientific description of the way in which the prescribed preventive measures should be applied from the epidemiological, security, economic, legal and other aspects, so that the society, through the mechanisms of the state, can defend and rehabilitate the consequences of an epidemic of an infectious disease. Eliminating the epidemic's impacts is a very difficult issue. In particular, there is an infectious illness epidemic that is spreading uncontrolled throughout society on the one hand. In order to introduce a quarantine that restricts the epidemic's progress and, if the quarantine lasts long enough, to end the epidemic, contact between members of the social group must be broken. On the other side, the cessation of communication between members of a social group also signifies the cessation of all facets of life in that society, including economic ties, education, growth of culture, scientific research, etc.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".