Gestão por resultados da administração pública: a experiência do estado do Ceará comparada ao modelo canadense
Bibliographic record
Abstract
The study has the objective of analyzing the elements of Ceará’s Model in comparison with the Canadian Model. The methodology used was a bibliography survey of the Results-Based Management (RBM) literature, followed by a document survey regarding both models as well as interviews with Canadian and Ceará managers. It presents that since 1987 the State of Ceará has been redesigning its management model in order to reach sustainable economic and social development. The study reveals that, in this process, Ceará researched for best practices of public administrations, being the RBM Canadian Model chosen as the reference for the design and implementation of Ceará ‘s new model in 2004. The study characterizes Canadian and Ceará’s models and does a comparative analysis of the two. Results show that they have the same concepts of planning policy and program with focus in results but they have differences regarding services, staff, administration, accountability, risk management, strategic governance, performance, learning and values. It is demonstrated that perceptions of Canadian and Ceará Managers reflect their culture and that the RBM concepts had been absorbed by the first ones but not completely by the last ones. The conclusion is that, excellent standards of efficiency, efficacy and effectiveness, demands the breakage of bureaucratic culture, whose process of changes is not easy or fast, and goes through the redefinition of values, qualification and evaluation of the public employee, a greater dissemination of RBM, as well as, the involvement of the population in demanding and following government results.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".