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Record W7087579901 · doi:10.7202/1118933ar

Together we make the neighbourhood

2024· article· en· W7087579901 on OpenAlexvenueno aff

Bibliographic record

VenueSens public · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsCommonsGrassrootsNeighbourhood (mathematics)AppropriationPoliticsSocial spaceSpace (punctuation)Social relationQualitative research

Abstract

fetched live from OpenAlex

A network of various self-managed community spaces played a crucial role in the citizen-led response to the social crisis during the first months of the COVID-19 crisis in the most vulnerable neighbourhoods in the periphery of Madrid. Nevertheless, such community spaces are under a precarious situation due to recurrent threats of closure from different administrations. It is therefore crucial to make visible the importance posed by such spaces in the construction of more resilient, equitable, and caring neighbourhoods. To that end, we propose the definition of a theoretical model of critical placemaking to understand how such grassroots practices are underlaid by a collective project of neighbourhood. The research fills the gap within different theories of placemaking, social innovation, and urban commons to establish a model based on the three axes: community, space, and political project. The study draws from the notions of relational place and civic engagement, together with models of space appropriation and social innovation theory. The theoretical model is contrasted with the qualitative research of the cases of social centres La Villana de Vallekas and Eko de Carabanchel . The results suggest an emergent city model at the neighbourhood scale of proximity self-managed citizen-led infrastructure that configures a resilient network against systemic and external threats.

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)0.001
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: none
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.034
GPT teacher head0.237
Teacher spread0.203 · 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
Published2024
Admission routes1
Has abstractyes

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