Circular Economy and the Role of Universities in Urban Regeneration: The Case of Ortigia, Syracuse
Bibliographic record
Abstract
Regeneration processes activate stable regimes of interaction and interdependence among the architectural, economic, cultural and social sub-systems in settlements. The thesis of this paper is that in order to progress towards sustainable and inclusive cities, urban governance should widen the decision-making arena, promoting virtuous circular dynamics based on knowledge transfer, strategic decision making and stakeholders’ engagement. The historic urban landscape is a privileged la b for this purpose. The paper adapts the Triple-Helix model of knowledge-industry-government relationships to interpret the unexpected regimes of interaction between Local Authority and Cultural Heritage Assets triggered in the late 90es by the establishment of a knowledge provider such as a Faculty of Architecture in the highly degraded heritage context of the city of Syracuse, Italy. Following this approach, the authors explain the urban regeneration happened over the last 20 years in the port city of Syracuse, based on knowledge sharing and resources’ protection that promoted processes of social engagement and institutional empowerment for both new residents and entrepreneurs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".