Impact of the Main Threats from COVID-19 on the Labor Market in the Context of Ensuring Economic Security
Bibliographic record
Abstract
The main research background involves identifying the factor that today, in the context of a pandemic and hostilities, the issue of labor migration, the labor market is an important problem for ensuring economic security. The main purpose of the article is to determine the major threats from COVID-19 and their impact on the labor market in the context of ensuring economic security. The basis of the methodology includes several theoretical methods for determining the key factors of influence and the method of graph theory and hierarchical ordering, which made it possible to structure the level of influence of these threats. The research process included identifying the main threats that negatively affect the labor market due to the onset of the pandemic and demonstrating the proposed methodological approach to streamlining their level of influence. Research conclusions shows that we have formed a model of hierarchical ordering of the influence of the principal threats from COVID-19 on the labor market in the context of ensuring economic security. Based on the results of the study, our suggestions are the proof of the fact that in modern economic conditions, theses of comprehensive research and monitoring of all negative factors that can affect the labor market are extremely relevant.
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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 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".