Lessons from Corviale: from the critical factors of Public Housing Plans towards a methodology for urban regeneration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it