Participatory citizenship in the making? The multiple citizenship trajectories of participatory budgeting participants in Brazil
Bibliographic record
Abstract
Most scholarship on participatory budgeting (PB) has highlighted its impact on democratic processes and redistributive outcomes, but there is also an implicit argument associating it with citizenship learning processes at the individual and collective levels. As a mechanism for social interactions, it is often called a ‘school of citizenship’ nurturing the development of ‘better citizens’ who participate as agents and members of a political community. This relation is, however, more ambiguous in practice. The article looks at this relationship and at the rise of so-called participatory democratic citizenship. Drawing from surveys conducted among PB participants in two Brazilian cities in 2009 (Porto Alegre) and 2014 (Belo Horizonte), the article shows that, at the individual level, multiple trajectories of citizenship can emerge among participants and can coexist in participatory processes. Contrary to the common wisdom, the article brings to light the complexity of differentiated citizenship learning processes among individuals active in participatory mechanisms. These cases thus show that PB does not necessarily contribute to the creation of a civic community, that is, a durable and active form of social organization that fosters the rise of a participatory and democratic citizenship.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".