Bibliographic Details
| Title: |
Analysis of Judiciary Expenditure and Productivity Using Machine Learning Techniques |
| Authors: |
Fernando Freire Vasconcelos, Renato Máximo Sátiro, Luiz Paulo Lopes Fávero, Gabriela Troyano Bortoloto, Hamilton Luiz Corrêa |
| Source: |
Mathematics, Vol 11, Iss 14, p 3195 (2023) |
| Publisher Information: |
MDPI AG, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Mathematics |
| Subject Terms: |
productivity, judiciary, machine learning, Mathematics, QA1-939 |
| More Details: |
Maintaining the judiciary requires a high level of budgetary expenditure, but the specifics of this relationship have not yet been fully explored. While several studies have examined the impact of spending on the judiciary through measures related to productivity and performance, none have used machine learning techniques. This study examines the productivity of the court system based on expenditures and other variables using machine learning techniques. In the clustering process Brazilian courts are ranked according to their productivity, while in the neural network step it is verified which characteristics are most relevant at the budgetary level related to judicial productivity for each cluster formed in the first step. The final neural network model supports the results of Pearson’s parametric correlation test, which found no significant correlation between expenditure and productivity. The findings from this study demonstrate the importance of understanding that increasing public budget expenditures alone is not sufficient to improve the efficiency of the judicial system. Instead, other administrative measures are necessary to meet the demands of the Brazilian judiciary and improve service delivery rates. These results offer important theoretical and managerial contributions to the field. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2227-7390 |
| Relation: |
https://www.mdpi.com/2227-7390/11/14/3195; https://doaj.org/toc/2227-7390 |
| DOI: |
10.3390/math11143195 |
| Access URL: |
https://doaj.org/article/3f7ea7139f2b4b759fec927f5c1f1842 |
| Accession Number: |
edsdoj.3f7ea7139f2b4b759fec927f5c1f1842 |
| Database: |
Directory of Open Access Journals |
