Modelos para la prevención de bancarrotas empresariales utilizados por el sector empresarial costarricense (Models for company bankruptcy prevention used by the Costa Rican business sector)
Bibliographic record
Abstract
El presente artículo ofrece un análisis de los modelos para la prevención de bancarrota más citados en la literatura, entre los cuales están: modelo Z score de Altman, modelo de Ohlson, modelo de Beaver, modelo de árboles de decisión y modelo DuPont. Además, incluye un estudio de los modelos utilizados por el sector empresarial costarricense, en el cual se evidencia el desconocimiento sobre el tema, ya que la mayoría de empresas investigadas no utiliza o conoce ningún modelo con la capacidad de prevenir las bancarrotas. En ese sentido, las herramientas más utilizadas son las razones financieras, control sobre el presupuesto y, en algunos casos, el esquema integral de rentabilidad (Dupont). Abtract This article presents an analysis of the models for bankruptcy prevention most cited in literature, that is, the Z-score model by Altman, the Ohlson 0-score, the Beaver method, the Decision Tree model and the DuPont method. It also includes a study of models used by the Costa Rican business sector that shows a complete lack of awareness of the subject, since most of it does not know or use any model for bankruptcy prevention. To this end, financial ratios, budget control and in some cases the DuPont integral profitability methods are the ones most used.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".