Brazil: Ministry of the Economy: Analysis of Key Functions and their Operational Macroprocesses: Benchmarking Operational Macroprocesses with Experiences from Canada, France, Mexico, Peru, Spain, the United Kingdom, and the United States
Bibliographic record
Abstract
This technical note compares the value chains and macro-processes of the Ministry of Economy (ME) of Brazil against the relevant management models and practices used by the ministries of finance, economy or equivalents in selected Latin American countries and the Organization for Cooperation and Economic Development (OECD). This analysis, carried out in the context of the creation of the MoU through the merger of five former ministries, aimed to help identify gaps in current practices and propose recommendations to improve macro-processes in Brazil. A team, including former finance ministers and experts from these countries, participated in the technical analysis and discussions with public officials in partnership with experts from the Inter-American Development Bank. The results show: (i) positive initial results after the merger in terms of policy coordination, coherence and efficiency; (ii) recent policy reforms in line with OECD practices, some of which have just begun to be implemented; and (iii) opportunities to continue improving management practices in selected macro-processes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".