Democratizing Regional Governance: Constraints and Opportunities for Brazilian and Other Metropolitan Regions in Latin America
Bibliographic record
Abstract
The scale of the region lies in between the local/city and the national/global, presenting challenges as well as new opportunities for democratizing planning and governance at the regional level. Most regional governance bodies and institutions suffer from democratic deficit in that their representatives are often appointed and far removed from direct constituents even though they decide on issues and concerns that directly affect the general public. Geographic, resource and economic inequalities between and within municipalities often work against inter-municipal cooperation. Gleaning from the Brazilian-Canadian collaborative action research, New Public Consortia for Metropolitan Governance, this paper will analyze why, how and under what conditions do forms of inter-jurisdictional cooperation work to promote democratization at the regional level, as well as vertical (citizen and governments, inter-governmental) and horizontal (inter-municipal) forms of accountability for the purpose of reducing poverty and social inequality. It will examine how Brazilian officials and bureaucracies can use as leverage the historic and institutional lessons learned from their practice of participatory budgeting, solidarity economy, and planning in promoting more democratic forms of regional governance. These lessons can draw insights for other Latin American metropolitan cities and municipalities dealing with similar regional governance and development issues. 1
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".