Building Brazilian citizenship in the context of poverty, waste, drugs and violence
Bibliographic record
Abstract
Subject area Social entrepreneurship, sustainable development and emerging economies. Study level/applicability Advanced undergraduate students and Graduate students (MBAs). Case overview We present the case of Marli Medeiros, a community leader in the city of Porto Alegre (south of Brazil) who has been working with the local government, local firms and local inhabitants over the last 40 years to build an organization that has been changing the reality of the slum Vila Pinto. The case highlights three main dilemmas faced by Marli Medeiros. Part 1 addresses whether to start a social entrepreneurship project in an environment surrounded by household violence and drug influences. Part 2 examines how to organize a community to develop this social project and challenge the context (local drug dealers). Part 3 considers how to work with different social players to innovate and manage a self-sustained social entrepreneurship that brings social change for an impoverished community. Expected learning outcomes Understand the five main characteristics required by social entrepreneurs to achieve social change by economic, self-sustained activities: social vision, sustainability guidelines, social networks development, search for innovation and search for financial returns. Understand the social entrepreneurship model from the point of view of a female leader in a local impoverished community. Understand and analyze the social and economic context of an emerging country. Supplementary materials Teaching note.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".