Urban regeneration through arts and culture: The case of a multicultural neighbourhood in a medium-sized Italian city
Bibliographic record
Abstract
While urban regeneration through cultural events and institutions is now an accepted practice in many cities, the debate on the cycle of culture-led policies (aims, process, outcomes, evaluations) has become more and more intense. The purpose of this work is to analyse the impact of culture-led policies on urban regeneration processes, through the analysis of Piacenza’s Quartiere Roma case study. The paper is divided in two parts: the first concerns a brief overview of the theory of culture as an instrument of urban regeneration where arts can play the role of a driving force. Literature on urban studies and empirical cases demonstrate that culture-led policies can trigger successful regeneration processes. In particular the most relevant outcomes consist not only in image change but also in social cohesion and resident community engagement. The second part focuses on Piacenza’s Quartiere Roma case study. From 2008 to 2010 the Agenzia Quartiere Roma was established to provoke regeneration in a low-income neighbourhood in Piacenza (Italy) mainly through cultural actions and integrated programmes. Over three years, events and other activities were organised by the agency to improve the quality of life and address the perception of insecurity in the community. The agency identified culture and the arts as the main force to challenge the problems of the neighbourhood. In particular the programme enabled new shop openings by young artists. Partnerships were established and some actions succeeded in rebuilding a new neighbourhood identity in terms of reduction of insecurity and social exclusion particularly through the main project called MUSA (Street Art Urban Movement).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".