Bibliographic record
Abstract
Urban regeneration should have a role of integration between planning and design activities; moreover, it very often looks like a single project with detailed proposals and performances, without a general vision of the area.It is therefore necessary to build a reference for the design choices, which primarily concern the city but increasingly involve the surrounding territory.This paper explores the relationship between the plurality of factors that exist on the settlements and productive activities assets which are the elements of the coastal landscape.Our work aims to deal with every aspect of the Re-generation potential, which is an opportunity for enrichment of urban planning, especially in the cases of the Regions -as Molise is -in which really cities do not exist but there is a "continuum" between adjoining Municipalities in a mainly rural territory.Moreover, coastal areas have been in recent years the privileged place for interventions guided by the principles of urban regeneration, in the first phase focused on the physical rehabilitation of degraded areas, afterwards including attention to cultural, social, economic and environmental aspects.In the evolution of this phenomenon, not only we need to highlight the shift from physical rehabilitation to urban regeneration, as an integrated process of actions with a focus on the social aspect, but also we must underline that the so-called "complex programs" -utilized for regeneration projects -are more dynamic than the traditional plans.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".