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Record W7081073456 · doi:10.6093/2284-4732/12645

The DE-Sign Urban Lab: the pilot case of Cosenza city, energy efficiency as a driver for social inclusion, resilience and integrated urban regeneration

2025· article· en· W7081073456 on OpenAlexaff

Bibliographic record

VenueUniversità degli Studi di Napoli Federico II · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsHorizon Health Network
Fundersnot available
KeywordsSustainabilityUrban resilienceUrban planningResilience (materials science)TrainUrban sustainabilityInclusion (mineral)Urban regenerationEfficient energy use

Abstract

fetched live from OpenAlex

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

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.843
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.013
GPT teacher head0.237
Teacher spread0.224 · 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.

Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

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