Civil Society and Social Innovation in Public Arenas in Brazil: Trajectory and Experience of the Movement Against Electoral Corruption (MCCE)
Bibliographic record
Abstract
Abstract In recent decades, the Brazilian Movement Against Electoral Corruption (MCCE) has been promoting social innovation in the public sphere, which led to mobilization towards the creation of two popular initiatives in Brazil: the “Law Against Vote-Buying” (Law 9840/1999) and the “Clean Record Law” (Complementary Law 135/2010). This paper explores how the collectives of MCCE engage in social innovation in the public arena of electoral corruption in Brazil. The analysis shows social innovation as a driving force of social change promoted by the association of a multitude of actor networks both in the long term and at the interface of macro and microscales of social reality. Therefore, social innovation in the Brazilian electoral corruption arena occurs simultaneously as a process and an outcome produced by the collective actions of different public groups that can reflect, organize and reform a cause, manage trial situations and create new solutions for this public problem.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.009 |
| 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".