Socio-Economic Risk Assessment and Peril Analysis in the Context of the COVID-19 Pandemic and Emergencies
Bibliographic record
Abstract
The article proposes a methodology for assessing socio-economic risks and analysing perils in the context of the COVID-19 pandemic and emergencies, and presents the results of its testing. The methodology allows assessing changes in labour risks by possible values of internal indicators. This makes it possible to develop scenario approaches in case of a change in quarantine restrictions or their lifting. Testing of the developed methodology for assessing socio-economic risks and analysing perils caused by the impact of the COVID-19 pandemic on the domestic labour market and employment is based on determining changes in economic conditions of risk emergence and occurrence, direct employment risks, and assessment of socio-economic consequences of risks in case of strict quarantine measures during the 2nd quarter of 2020. A feature of the proposed scientific and methodological support for assessing socio-economic risks and analysing perils in the context of the COVID-19 pandemic is taking into account the basic principles of the European system of social indicators using quantitative methods of assessing social risks in the workplace. The practical significance of the obtained results is that the introduction of the developed scientific and methodological support, and practical recommendations will promote the development of preventive measures to deal with socio-economic risks and perils in the domestic labor market and employment, to improve social protection during the COVID-19 pandemic, and to prioritise the government measures based on the risk ranking according to the occurrence, and the impact probability.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".