The Hierarchy Level of Strategy: A Bankruptcy Prediction of the Company using the Altman Z-Score Method in the Coronavirus Disease Period (An Empirical Study on Manufacturing Companies of Various Industry Sub-Sectors Listed in Indonesia Stock Exchange in
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Bibliographic record
Abstract
Coronavirus disease (COVID-19) weakens many business sectors, including the corporate sector.This research uses quantitative method with explanation theory. Thus, researchers are interested in finding outwhether there are differences and influence of financial conditions in the 1st and 2nd Quarter of 2020 onmanufacturing companies of various industry sub-sectors listed in Indonesia Stock Exchange. This researchutilized the Altman Z-Score method. In addition, method of analysis used multicollinearity and binary logisticregression, which data sources were from financial reports in the 1st quarter and the 2nd quarter of 2020 with asample of 36 x 2 = 72 observations; utilizing purposive sampling technique. The results showed that there wasno difference in financial conditions in the 1st quarter and the 2nd quarter. However, there was a significanteffect on the variables studied. Therefore, the Altman Z-Scores method is proven to be able to predict financialbankruptcy of manufacturing companies of various industry sub-sectors. Additionally, this research provides acontribution for companies carrying out hierarchical strategies to gain a competitive advantage.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it