Bibliographic record
Abstract
What do we mean when we talk about public space? We asked some privileged testimonials during the research programme of relevant national interest (PRIN 2009) titled: "Public spaces, mobile populations and processes of urban reorganisation". Among the questions in the in-depth interview on relations between urban populations and public space, we asked for a definition of public space and the identification of some significant spaces in the city of Turin, in the City Centre and the San Salvario district and the quarter of Barriera di Milano closer the outskirts of the city. The definition of the concept of "public space" highlighted certain significant aspects. One of these concerns a sort of standardisation among the various affirmations, that identify public space as an, albeit, weak space for socialisation, to which certain thoughts on the role of commerce and new media are linked. Another emerging and opposing aspect emphasizes a clear difference between the different views, which depends largely on the professional and cultural experiences of each interlocutor. In summary are recognized some different approaches: public space as a relational space, "cappuccino" space, weak sociality space, new relational spaces and a new type of public space: the "District Houses". Photos of Turin public spaces and interview passages are reported
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.
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 | 0.025 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.016 | 0.116 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".