Participatory Democracy in Latin America
Bibliographic record
Abstract
Despite the recent expansion of innovative democratic experiments, scholars have observed that much of what has been written focuses on theoretical issues and normative debates. There is relatively little empirical research and a lack of comparative work that contrasts different models of citizen participation.These data were collected as part of a PhD thesis project intended to addresses these gaps in the literature. The project compares local participatory mechanisms in three countries with different models of participatory design: Venezuela’s “radical” participatory democracy, Chile’s “pragmatic” efforts at expanding participation and Ecuador’s “hybrid” model that demonstrates features of both. Drawing on extensive fieldwork, it compares outcomes produced by participatory mechanisms in these countries and identifies five factors that enhance or diminish their ability to generate positive outcomes: decision-making and implementation capacity, quality of deliberation, inclusiveness, levels of engagement and the nature of relationships between participatory mechanisms and local authorities. The findings reveal that the “radical” model does not produce significantly better outcomes despite its promises of deepening the quality of democracy. Institutional design and state discourse on democracy are therefore less important than the extent to which these five factors are present. The data include interviews and surveys.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".