Changes in industry and services sectors in Poland during the COVID-19 pandemic
Bibliographic record
Abstract
In 2020, the World Health Organization announced the global COVID-19 pandemic, which was followed by unprecedented constraints on society and the economy. The restrictions imposed had an impact on the transformation in industry and services sectors. The pandemic, however, affected particular industry sections and types of services to a different degree. Overall, the industry sector is assumed to have been less affected by the crisis, because the government restrictions did not embrace industrial production for the most part. Services faced a different situation; some of them were not provided due to top-down decisions. Therefore, the vulnerability of industry and services to such a strong external impact varies. The article aims to identify the degree and trends in changes in industry and services sectors during the COVID-19 pandemic and to determine the regularities stemming from a different degree of vulnerability of both sectors to such a powerful external stimulus. The study examines changes occurring in different industries and types of services (by the Polish Classification of Economic Activity/NACE 2.0) by way of statistical indicator analysis and using Statistics Poland and Eurostat data. The conducted analysis of the COVID-19 pandemic influence on the industry and services sector leads to the conclu- sions that its impact on the industry sector was very time-limited—a sharp fall in gross value added in industry oc- curred mainly in the second quarter of 2020. The pandemic had a modest effect on industry employment, primarily as a result of anti-crisis shields and the will to maintain the potential of labour resources. In the services sector, accommo- dation and food services suffered the most. The significant falls were noted in the transport section as well as cultural, sports and personal services. Business services which were transferred to the Internet and were provided online ended up the most resilient. The ultimate winner of the pandemic is ICT services, especially electronic ones, which have re- placed, wherever possible, traditional types of services.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".