The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods
Bibliographic record
Abstract
Many studies point to the impact of the COVID-19 pandemic on the socio-economic situation of countries and, consequently, on the achievement of sustainable development goals. Although termed a health crisis, the pandemic has also had an impact on the labour market. The imposed restrictions caused companies to close or reduce their operations. Employees switched to remote work, but also often lost their jobs temporarily or permanently. However, the impact of the pandemic on the labour market is not so obvious. This is indicated by our research and that of other researchers. In this paper, we used individual data on the unemployed registered at the labour office in Szczecin (Poland) and were thus able to apply survival analysis methods. These methods allowed us to assess changes in the duration of unemployment and the intensity of taking up work for individual cohorts (unemployed people deregistered in a given quarter). The results indicate, on the one hand, the problems in the labour market during the pandemic and, on the other hand, the adapted reaction of the unemployed to the situation and the acceleration of the decision to accept an offered job.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".