Reframing spatial practices in processes of social inclusion of migrants and local communities in the reactivation of historic urban landscapes.
Bibliographic record
Abstract
In recent years, there has been an unprecedented exodus of persons displaced from conflict torn regions to Europe. In the third quarter of 2016, about 183,000 people were granted protection status by the European Union member states (Eurostat, 2017) that allowed them to become refugees. This is by far the most significant influx of people the European continent has experienced since WWII. So far, current European public urban management, planning models, urban and architectural design methodologies have proven to be unable to give creative, dynamic and sustainable responses to this complex and rapidly evolving crisis. In this context, it is crucial to reframe the role of architecture and spatial practices, as agents of social and spatial inclusion and regeneration at different scales. We are interested in analysing how participatory design strategies might facilitate a collective engagement of diverse communities in the reactivation of historic urban landscapes and how this foster the production of local social capital (Gaunlett 2011), nurturing empathetic communities and delivering ‘collaborative inclusion’ (Manzini, 2015) as a mean to assert their civic identity. This paper explores the potential that tactical urbanism and tactical placemaking have in articulating inclusive processes of spatial production in the public realm that might enable migrants, refugees and locals in risk of social and spatial exclusion, to participate in the (re)definition and (co)production of social space, fostering their active participation as an agent of their own resilient empowerment.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.051 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| 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".