Clustering analysis of financial distress on tourism sector companies go-public due to lSSR
Bibliographic record
Abstract
Large-Scale Social Restriction Policy (LSSR) to prevent the spread of COVID-19 has a big impact on economic activities, one of which is activities in the tourism sector. Restrictions on outdoor activities reduce the productivity of companies that can lead to bankruptcy. By knowing the financial condition of the company, we can predict whether the company will experience financial pressures or not. This paper tries to analyze the grouping of 100 companies in the tourism sector before (the first quarter of 2020) and after (the second quarter of 2020) the application of LSSR conditions. This paper uses the K-Means grouping method and the financial ratio of each company. Then, the variables in the analysis are Return on Asset (ROA), Total Asset Turn Over Ratio (TATO), Debt to Equity Ratio (DER), and Price to Earning Ratio (PER). The results showed that in the second quarter of 2020 or after the implementation of LSSR, almost all companies tend to be in a financially depressed condition. The number of companies that are under financial pressure after the implementation of this policy is 98 companies.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".