A Methodological Approach for Evaluating Brownfield Redevelopment Projects
Bibliographic record
Abstract
In recent decades, municipalities around the world have been developing community policies and seeking to apply them in their cities. They use methods for exchanging information and opinions on decisions, policies, plans and strategies and involve and consult with the community and stakeholders in all aspects of the decision-making process. The application of methods for thoughtful planning has become the goal of policy makers to improve the lives of citizens and stop the expansion of the city into the countryside. The aim of this article is to integrate the notion of sustainability into a methodological approach, taking into account the actors involved in the decision-making phases, the objectives, and the local indicators in an urban redevelopment project (brownfield). Our approach is based on an analysis of 21 articles and on a transversal and cross-cutting view of the interdisciplinary themes of sustainable development by inserting the main actors into decision-making in urban projects and by selecting local indicators. We put in place a methodological approach for the evaluation of urban projects that takes into account local expectations. The goal is to identify and classify the elements that are needed for decision making, including the indicators related to environmental and socio-economic components, in order to develop an effective evaluation tool. This research contributes to the knowledge of project evaluation tools in the specific context of a city.
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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 | 0.254 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".