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)
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
<p>El presente artículo ofrece un análisis de<br />los modelos para la prevención de bancarrota<br />más citados en la literatura, entre los cuales<br />están: modelo Z score de Altman, modelo de<br />Ohlson, modelo de Beaver, modelo de árboles<br />de decisión y modelo DuPont. Además, incluye<br />un estudio de los modelos utilizados por el<br />sector empresarial costarricense, en el cual se<br />evidencia el desconocimiento sobre el tema,<br />ya que la mayoría de empresas investigadas<br />no utiliza o conoce ningún modelo con la<br />capacidad de prevenir las bancarrotas. En ese<br />sentido, las herramientas más utilizadas son las<br />razones financieras, control sobre el presupuesto<br />y, en algunos casos, el esquema integral de<br />rentabilidad (Dupont).</p><p> </p><p><strong>Abtract</strong></p><p>This article presents an analysis of the<br />models for bankruptcy prevention most<br />cited in literature, that is, the Z-score model<br />by Altman, the Ohlson 0-score, the Beaver<br />method, the Decision Tree model and the<br />DuPont method. It also includes a study of<br />models used by the Costa Rican business<br />sector that shows a complete lack of awareness<br />of the subject, since most of it does not know or<br />use any model for bankruptcy prevention. To<br />this end, financial ratios, budget control and<br />in some cases the DuPont integral profitability<br />methods are the ones most used.</p>
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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