Design for Social Innovation in Italian Inner Peripheries
Bibliographic record
Abstract
Italy is scattered with small peripheral settlements, often characterised by difficult environmental morphologies, a lack of public services and a tendency toward depopulation. These places\nare mostly located in inland mountainous or island areas, far away from big cities. Yet despite a significant drop in population, they are still home to a quarter of the Italian population, distributed over more than two thirds of the entire country. Today, a few of these towns are being reorganised and repopulated, re-establishing a sustainable community approach thanks to innovative forms of organisation and entrepreneurship, capable of bringing together cultural, natural and social capital and production chains. In these contexts, unexpected models of innovation and design are born, to outline peculiarities of extreme interest for a contemporaneity that comes to include the dramatic instances of the current pandemic circumstances.\nThe aim of this paper is to draw attention to strategic scenarios, theoretical guidelines and examples of good design practices, already created or in progress, including those by the authors, related to the promotion of eco-literacy, community and on-demand health and social services, the promotion of local agri-food systems, the preservation of know-how and craftsmanship, highlighting the contribution that articulated and multiscale design can provide in transforming territorial fragility into social and economic opportunities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".