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Record W7027549348

Design for Social Innovation in Italian Inner Peripheries

2022· article· en· W7027549348 on OpenAlexaboutno aff

Bibliographic record

VenueFlorence Research (University of Florence) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDiverse academic and cultural studies
Canadian institutionsnot available
Fundersnot available
KeywordsPromotion (chess)Social capitalCapital (architecture)Quarter (Canadian coin)Social innovationNatural (archaeology)Pandemic
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.229
GPT teacher head0.299
Teacher spread0.069 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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