Governança colaborativa nas Américas: uma análise da governança metropolitana a partir dos exemplos de Vancouver (Canadá), Guadalajara (México), e Natal (Brasil)
Bibliographic record
Abstract
This thesis aims to investigate political conditions that may facilitate or hinder the collaborative metropolitan governance from three metropolitan areas, based on design features, implementation and management: Vancouver (Canada), Guadalajara (Mexico) and Natal (Brazil). Although different in their particular realities, these regions have experienced common metropolitan challenges with regard to the rapid population growth in the metropolitan area and increased demand for public services, urban mobility, threat to natural resources and the environment, coordination challenges and articulation of the metropolitan region to the federal level and the urban governance required for the sustainable development from territory. The Metro Vancouver, in British Columbia, Canada, considered the most successful model of collaborative federation in conjunction and cooperation of local governments at the regional level and control of resources and provision of public services has shown progress in refers to the process of decision-making guided by consensus, especially on water governance. The recent debate on collaborative governance as an array of government that demand collective decisions guided by consensus allowing the strengthening of the institutional capacity of governments is used as a theoretical reference. The concept of collaborative governance is also associated with inclusive and consensual democratic governance towards engagement of the private and non-governmental actors in decision-making guided by consensus.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".