A Sequential Probabilistic System for Bankruptcy Data Classification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the last decade, the development of bankruptcy prediction models has been one of important issues in accounting and corporate finance research fields. Indeed, bankruptcy is a critical event that yields important loss to management, shareholders, employees, and also to government. Statistical methods such as discriminant analysis, logistic and probit models were widely used for developing bankruptcy prediction systems. However, statistical-based approaches are assumes strong assumptions including linearity of the relationship among dependent and independent variables, normality of the errors which limit their applicability in bankruptcy real world problems. Recently, machine learning and soft computing techniques including artificial neural networks, support vector machines, and evolutionary intelligence have brought forth new alternatives in solving nonlinear problems with applications in bankruptcy prediction. The purpose of this chapter is to present a sequential probabilistic system for bankruptcy data classification to help manager in making decisions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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