Lessons from Corviale: from the critical factors of Public Housing Plans towards a methodology for urban regeneration
Bibliographic record
Abstract
This paper is part of Urban Studies PhD research that seeks new approaches of urban regeneration in ongoing interventions in social housing neighbourhoods in Italy and Portugal. Corviale is here taken as case study assessed with a ‘zoom-out methodology’, that means to expand the analysis from the case study to Rome regarding the construction of the ‘public city’ and the regeneration of public housing neighbourhoods. On one hand, Corviale allows comprehension of the critical factors of Public Housing Plan (PEEP) in Rome: large dimensions, massive housing concentration, high execution speed, incapacity of the public management, under-use of the public assets and unfinished services. On the other hand, the interventions featured in the case study display a strategy for the urban regeneration through three points: densification of the existing housing stock; solution to the squatting that does not involve forced evictions; and participation by way of the “Laboratorio di Città Corviale”. The case study sheds light on the past stages of the Italian public housing and recognises a model for urban regeneration of public housing. The research identifies public housing neighbourhoods as an ideal ground of investigation and action to develop new methods of urban planning.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".