Assessing risk management in Brazilian social projects: a path towards sustainable development
Bibliographic record
Abstract
Social projects are an important mean to reduce social problems and they have an increased relevance in contexts of high social inequalities, as it is the case of Brazil. However, the existence of these projects may not be enough, they need to be properly managed, including projects risks. In this context, this paper aims to evaluate the application of risk management in social projects in Brazil. A survey was performed with social project managers and data was analyzed through TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), to rank the items application levels, according to the ISO 31,000’s guidelines. Results provide an overview of Brazilian situation. According to the findings presented, the items related to projects context analysis, and responsibility assignments in the projects were the items better applied, while the items related to the understanding of team members regarding risks in social projects and to the amplitude of risk management in the projects were the items with the worst rank positions. The findings presented here contribute to expand the debates on the subject.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".