Anti-poverty and progressive social change in Brazil: lessons for other emerging economies
Bibliographic record
Abstract
This article examines Brazil’s experiences with anti-poverty and progressive social change, and spells out possible lessons for emerging economies with similar challenges. It draws on the Bolsa Familia conditional cash transfers (CCT) and the continuous cash benefits programmes and discusses important aspects of programme leadership, management and coordination. After a brief discussion of poverty, it presents a framework synthesizing key success factors for effective and sustaining programme implementation. Brazil does not offer a ‘blueprint’ for other countries to copy; only lessons from experience. Therefore the article concludes by discussing key ongoing challenges and suggests areas for future research, focusing on comparative studies across countries. Points for practitioners Progress has been made against global poverty, notably in countries experiencing sustained economic growth like Brazil. In spite of these remarkable efforts, challenges remain especially for countries which focus only on macroeconomic growth but not equity or inclusive development. Growth without equity does not eradicate poverty. Accordingly, emerging economies are being urged to pursue multipronged strategies: crafting innovative public policies, reshaping institutions for macroeconomic management, reaching out and engaging target communities, democratization, legislated and constitutionally mandated progressive social change. This article provides practical lessons from experience from Brazil, which practitioners from other emerging economies can adapt to their own circumstances for the effective and sustaining implementation of anti-poverty and progressive social change. It also provides a holistic framework for better understanding the institutional context, leadership, management, inter-government and cross-sectoral coordination and private sector participation. Finally, it identifies some of the key ongoing challenges in Brazil, and suggests areas for applied comparative research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".