The impact of the COVID-19 pandemic on the due payments of Polish entreprises from selected industries
Bibliographic record
Abstract
The emergence of the COVID-19 pandemic has undoubtedly caused many perturbations, at the same time hindering the functioning and operation of enterprises from various industries, which, due to the often inability to conduct business, found themselves in a very difficult financial situation, with a difficult ability to settle their liabilities. Too high share of receivables that are not settled in a timely manner can result in various problems for enterprises, including, in particular, financial problems that can lead to large-scale bankruptcy. Considering a huge number of connections between individual entities, the bankruptcy of one may pose a risk of a wave of bankruptcy of others. The paper aims to analyze the impact of the COVID-19 pandemic on the payment situation of Polish enterprises. The research was conducted on the basis of an analysis of data on the payment situation of Polish enterprises from selected industries. Basic descriptive statistics was used in the study to characterize the material. The non-parametric Wilcoxon pair order test, which is the equivalent of the Student’s t-test for related variables, was used for the research. The research proved that at enterprises from almost every industry, the value of debts at the end of the second quarter of 2020 was higher than in the first quarter. It can therefore be concluded that the outbreak of the pandemic contributed to an increase in arrears, which, in turn, resulted in an increased risk of doing business. The greater the share of arrears with contractors, the greater the risk of financial problems at the enterprise, and hence the increased risk of bankruptcy.
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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 | 0.001 | 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) | 0.002 | 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".