Knowledge Acquisition and Meaning‑making in the Participatory Budgeting of Local Governments
Bibliographic record
Abstract
The participatory budgeting of Brazilian municipalities is one of the most publicized examples of democracy‑in‑action that fosters citizenship knowledge acquisition in local government. This research adds two new perspectives to the participatory process. The first one demonstrates how knowledge is acquired and shared, and which are the new contextual conditions for this acquisition to happen, which we call, the knowledge acquisition process. At second, we explain the meaning‑making process as a subjective way to acquire knowledge about oneself, the others, and the democratic systems. Citizens, public servants, policy‑makers, and politicians acquire new meanings for the role they play in local government and become reengaged on democracy through the ontological change from representative to participatory and deliberative democracy. In this qualitative research, our methodological bricolage relies on the triangulation of methods (i.e. documentation review and observations of behaviors and procedures; in‑depth‑interviews; and, focus groups) and sources (i.e. comparison of cases, and stakeholders). The bricolage deconstructs these social actions on their constituents, vis‑a‑vis hidden intentions; instruments; and, implementation. Among the several Brazilian cases, this longitudinal study (i.e. 12 years) concentrates on five of them. Our findings suggest that the participation in the process allows for a change on individuals’ understanding of democracy, enhances citizenship knowledge acquisition, reframes the meaning of citizenship, and improves the quality of relationships among the stakeholders involved. In the participatory budgeting, citizens learn, what critical theory calls “to emancipate”. They act as if they were members of an ideal kingdom of ends in which they were both subjects and sovereigns at the same time, which guarantees citizenship engagement, learning and development.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.011 |
| 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".