Analysis of the Public Management Profile in Brazilian Municipalities and the Differentials Found in Smart City Management
Bibliographic record
Abstract
Global population growth is a subject of debate in governmental, non-governmental organizations, and academia, as well as a concern for contemporary society. The projections of weakening and depletion of urban infrastructure, difficulties in supply, scarcity of natural resources, associated with primary needs (health, education, housing, and security) created a demand for alternatives to attending the population needs. Some cities, called smart cities, already stand out for achieving their management goals with the help of technology. This article aims to identify the management elements that differentiate the management of a city considered smart (Belo Horizonte, Minas Gerais, Brazil) from the others in the Brazilian context. For the analysis, secondary data for 2015 provided by the Brazilian Institute of Geography and Statistics were used. The analysis allowed us to identify the management characteristics of the 5,570 Brazilian municipalities. The characteristics of the management of the municipality of Belo Horizonte, considered a smart city, were identified through interviews with the managers of the municipality. With the analysis, we identified the differentiating elements of the management models, which made it possible to create environments that propelled the increase of creativity and proposals of technological bases in the management of a smart city.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".