The Economic Evaluation of Projects as a Structuring Discipline of Learning Processes to Support Decision-Making in Sustainable Urban Transformations
Bibliographic record
Abstract
This paper is based on the following research questions: i) In which way could the discipline Economic Evaluation of Projects contribute to conveying the sustainability concept in urban settings among master’s degree students? What are the methods/techniques that can support decision processes of sustainable urban transformation? In response to the two research questions, the paper proposes a multi-methodological framework as a design tool for students (future professionals) aimed at representing the decision problem from a sustainable planning perspective. Through a Problem-Based Learning approach based on a case study, the proposed framework considers: SWOT Analysis, Stakeholder Analysis (SA), Multicriteria Analysis (MCDA), Cash Flow Analysis (CFA), and the application of the Neighborhood Sustainability Assessment Tools (NSATools). The multi-methodological framework has been applied to an experimental teaching case study as part of the Economic Evaluation of Projects module demonstrating its effectiveness in terms of sustainable spatial planning and structuring of the decision process from a multi-actor perspective. Future directions of the research are aimed at tackling two major limitations of the multi-methodological framework as the need to closely reflect a real decision process through an iterative framework and the sometimes hard interpretation of some elements of urban sustainability.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".