Methodological Strategy for the Recovery of Overdue Portfolio in the Textile Sector
Bibliographic record
Abstract
The overdue portfolio is the accounts receivable that a company has, whose payment date has already expired and has not yet been collected. Its proper management represents a fundamental aspect both in the financial planning and in the entity's growth, as it reflects possible significant risks in its finances. This study aims to propose a strategy for the mitigation of the overdue portfolio and its economic impact on the textile sector. In this sense, it is evident that the null or deficient policies of cash sales or credit, and of the latter in turn of extrajudicial and judicial collection policies, affect the correct presentation of the financial statements, directly affecting the company's economy, which leads to stagnation in production and sales. Therefore, the need to prioritize efficient collection management is corroborated. The descriptive methodology was used through four stages for the interpretive analysis of bibliographic documents. Theoretical methods such as logical history, synthesis analysis, inductive deductive, and empirical were used. Finally, as a result, a reference framework is obtained on which alternatives, and corrective actions can be managed and implemented that will allow the business sector, as well as the accounting, financial and legal areas, to enhance their strengths, for the proper management of sales and the prevention of uncollectible overdue portfolio.
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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 | 0.042 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".