Impacto da gestão para resultados nos indicadores criminais do Ceará: uma análise prévia e posterior à lei estadual n° 15.558/2014
Bibliographic record
Abstract
The actions against crime is a constant in the promises of political campaigns and government plans, but the feeling of the population is less secure every day - despite the high Brazilian tax burden. This governmental problematic aim to how to provide better results in public policies in the area of security. The methodology called Results-Based Management (RBM) emerges as an alternative applied in several states of the federation (eg MG, PE, SP, ES, RN and DF), as well as in municipalities (eg Rio de Janeiro and Belo Horizonte) and in other countries (eg Canada, United Kingdom, Australia, New Zealand, Chile, Bogota, England and Wales, South Africa and Sri Lanka). In this sense, the Government of the State of Ceará initiated in 2003 the adoption of RBM and has been seeking to improve its practices, resulting in actions such as the implementation of the Compensation System for Achieving Targets by Strategic Indicators of Crime in Ceará (Law N° 15,558, of 11/03/2014) by the Secretariat of Public Security and Social Defense (SSPDS). Thus, the opportunity arose to evaluate academically this public policy approach that makes use of the RBM model and its impact on the society of Ceará. Based on data from the 184 municipalities of Ceará between 2010 and 2016, the aim is to evaluate crime indicators and their influencing aspects before and after the implementation of the RBM model by the SSPDS. To analyze this effect, a methodology will be used based on the determinants of crime and its function structured in data panel. The estimators studied are those of Fixed Effect and Random Effect, as well as that of Fractional Generalized Least Squares. Although the expectation of Law N°. 15,558 / 2014 is to directly and negatively influence the crime of Ceará, estimates indicate that this result has not been reached and that this perspective is not a singularity of Ceará, since the same fact can be observed by other Brazilian and international experiences. On the other hand, this mechanism, because it allows direct financial gains, can be responsible for raising the effort in local security, which starts to act more intensely and, consequently, starts to register a greater number of occurrences.
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How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".