Influence of the COVID-19 pandemic on the transition of people on the Polish labor market – hidden threats
Bibliographic record
Abstract
Abstract The article analyzes changes on the Polish labor market after the outbreak of the COVID-19 pandemic. It aims to assess the effect of the SARS-CoV-2 pandemic on the transition of people on the labor market in Poland. The decided majority of research into the effects of the COVID-19 pandemic on the labor market was based exclusively on resource analysis, omitting stream analysis. This research fills this gap and provides analysis by quarter of the transition of people on the labor market between employment, unemployment, and professional inactivity. The COVID-19 pandemic caused a severe drop in the number of people in work in the second quarter of 2020, similar in level to an analogous increase in the number of people professionally inactive. The effects of the pandemic were much more severe for women than for men. Detailed analysis of transitions on the labor market shows that around 50% of jobs lost due to the outbreak of the pandemic were not regained. Many redundancies were permanent, which may translate into a weakening of the dynamic for the recovery of the labor market in the future.
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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 | 0.000 | 0.002 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".