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Urban Planning in Recife, Brazil: Evidence from a Conflict Analysis on the New Recife Project

2017· article· en· W2613968478 on OpenAlexafffund
Maísa Mendonça Silva, Keith W. Hipel, D. Marc Kilgour, Ana Paula Cabral Seixas Costa

Bibliographic record

VenueJournal of Urban Planning and Development · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsWilfrid Laurier UniversityCentre for International Governance Innovation
FundersUniversidade Federal de PernambucoUniversity of Waterloo
KeywordsOpposition (politics)PoliticsRanking (information retrieval)Local governmentArmed conflictOrder (exchange)Conflict resolutionBusinessUrban planningPolitical scienceEnvironmental planningManagement sciencePublic relationsPublic administrationComputer scienceGeographyEconomicsEngineeringLawCivil engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.304
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2017
Admission routes2
Has abstractyes

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