The DE-Sign Urban Lab: the pilot case of Cosenza city, energy efficiency as a driver for social inclusion, resilience and integrated urban regeneration
Bibliographic record
Abstract
The DE-Sign Research Group is leading an initiative under Italy’s ‘Italy in Class A’ program to promote energy-efficient urban design and regeneration. Focusing on Cosenza (but as a model that can be replicated on a national scale), the project aims to create sustainable, inclusive urban spaces by integrating off-site construction, energy-efficient public housing, and innovative urban planning (NBS). The project involves various stakeholders, including citizens, students, and local organizations, in co-designing a Masterplan for the Vaglio Lise area, transforming it into a mobility hub and a sustainable community. The initiative emphasizes ‘proximity energy’, fostering social inclusion and democratic access to energy. The project’s communication model, ‘Approach, Enable, Act’, ensures broad participation and shared planning, setting a national example. The Urban Laboratory serves as an experimental platform, engaging citizens—especially youth—in co-design processes that link public policy with community needs. The KDZENERGY project, part of this initiative, trains students in energy sustainability and co-design, making them active contributors to their neighborhoods transformation. Keywords: energy-efficiency, urban design, 3A-model, masterplan, urban regeneration
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".