Bibliographic record
Abstract
Nowadays, entrepreneurs can very easily find themselves in a bad financial situation. That is why it is essential to look for ways to help them effectively. However, this cannot be done without knowing the real situation in each country, which is the basis for creating the appropriate conditions. The study's main objective was to identify and compare the development of insolvency indicators representing the bankruptcy of entrepreneurs in Slovakia and the Czech Republic from the first quarter of 2017 to the second quarter of 2021. Descriptive analysis and primary research methods were used to achieve this objective. The analytical processing dealt with the indicator of initiated bankruptcies. A fluctuating decline was observed in both countries. In 2020, a slight increase was recorded, and a further increase can be expected due to the coronavirus disease 2019 (COVID-19) pandemic. 2018 and 2017 were the least positive years for Slovakia and the Czech Republic, respectively. The results indicated the need for interventions in the business environment of both countries to help entrepreneurs in bad situations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".