Bibliographic record
Abstract
Urban commons represent a unique opportunity for public authorities to proactively tackle the dismantling, abandonment and obsolescence of the built heritage generated in cities by social and economic transition processes (affecting both private and public heritage). In a moment when resources and opportunities are lacking, and deep changes are taking place in the real estate market dynamics, strengthening a UC-oriented perspective could help public authorities blend their direction, coordination, intervention and direct territorial investment action as they strive to gain more accountability. As a start, such an approach could focus on publicly owned properties, calling for a change in their valorisation and mobilization strategies: the attention no longer turns to (often failing) economic tools, but mostly to define new local development pathways where social, generative and usage values come into play. It is hence necessary to establish new definitions, categories and descriptions for public property, focusing on its potential as a trigger for new urban regeneration processes and urban commons generation. Based on an exercise of mapping the city of Turin’s public properties, this contribution discusses how information on these assets is currently collected and systematized, exploring the limits and opportunities of assessing vacant properties at city scale through data analysis and mapping.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".