Urban Planning in Recife, Brazil: Evidence from a Conflict Analysis on the New Recife Project
Bibliographic record
Abstract
An urban planning conflict, the New Recife Project (NRP), located in the city of Recife, Brazil, is analyzed by means of the Graph Model for Conflict Resolution in order to obtain strategic insights. The NRP conflict has been under way since 2012 and is investigated with respect to its current status in 2016. The dispute is modelled as follows: four decision makers (NRP support, NRP opposition, Recife local government, and judicial authorities), two graph models, and the preferences of Recife local government are evaluated according to four criteria (economic, environmental, social, and political) by a multiple-criteria method, the preference ranking organization method for enrichment evaluations (PROMETHEE). Results suggest that lack of planning and public participation are critical issues in this Brazilian urban project and that both could improve decision making and prevent conflicts. Finally, analyzing this urban project conflict strategically also provides a reliable diagnosis of the situation and may help decision makers in future decisions and furnish guidelines to improve urban planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".