Evaluation of the operational viability of forensic units in Brazil: A hybrid approach based on the BWM and R-TOPSIS
Bibliographic record
Abstract
Public security is an area of increasing importance in Brazil, as society requires that public resources are managed more efficiently and effectively. Criminalistics is an integral and vital part of the Brazilian public security system and requires new management tools to optimize human resources, equipment, and facilities allocation. Faced with a challenging scenario of budgetary constraints in several areas in public administration, the search for innovative methods should be a priority for the forensic service sector managers. The current article presents a multicriteria decision model to evaluate the operational viability of 23 forensic units within the Federal Police of Brazil (PF). The framework used the hybrid approach BWM and R-TOPSIS. The proposed model led to the complete ranking of 23 local forensic units. Amongst the last positions in the ranking, it was possible to recommend merging or shutting down some units. The sensitivity analysis performed did not show abrupt variations in the original positions, confirming the robustness of the proposed solution. It was concluded that the model allowed resources optimization whilst not compromising the quality of the services provided to society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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 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".