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Record W3183937302 · doi:10.5821/ace.16.46.9893

LABTUR: una contribución metodológica a las prácticas de co-creación del espacio público

2021· article· es· W3183937302 on OpenAlexaff
Ana Carolina Salvador Taveira Cardoso, Alexandra Paio

Bibliographic record

VenueACE Arquitectura Ciudad y Entorno · 2021
Typearticle
Languagees
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsAS Composite (Canada)
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

La ciudad ha sido escenario de debates en las últimas décadas, fruto de la creciente urbanización y la consecuente complejidad de los problemas urbanos, que imponen profundos cambios en la forma de concebir, producir y gestionar el espacio urbano. Los laboratorios urbanos emergen como entornos experimentales e de aprendizaje colaborativo, explorando nuevos caminos para la producción compartida de conocimiento y soluciones a los problemas urbanos actuales, promoviendo la inteligencia colectiva basada en experimentos locales probados dentro de una cultura específica y un contexto situado. Partiendo de la idea de un laboratorio urbano experimental, esta investigación tiene como objetivo presentar la primera edición de LABTUR, desarrollada en el programa TUR: Co-creating Public Spaces, un proyecto de investigación-acción llevado a cabo en un contexto académico, en colaboración con el Ayuntamiento de Cascais, en Portugal. El artículo presenta los resultados de LABTUR y discute el aprendizaje basado en las dinámicas de co-creación, contribuyendo a la definición de una metodología colaborativa que promueva el diseño de un espacio público más sostenible e inclusivo. La ciutat ha estat escenari de debats en les últimes dècades, fruit de la creixent urbanització i la conseqüent complexitat dels problemes urbans, que imposen profunds canvis en la forma de concebre, produir i gestionar l'espai urbà. Els laboratoris urbans emergeixen com a entorns experimentals i d'aprenentatge col·laboratiu, explorant nous camins per a la producció compartida de coneixement i solucions als problemes urbans actuals, promovent la intel·ligència col·lectiva basada en experiments locals provats dins d'una cultura específica i un context situat. Partint de la idea d'un laboratori urbà experimental, aquesta investigació té com a objectiu presentar la primera edició de LABTUR, desenvolupada al programa TUR: Co-creating Public Spaces, un projecte d'investigació-acció dut a terme en un context acadèmic, en col·laboració amb l'Ajuntament de Cascais, a Portugal. L'article presenta els resultats de LABTUR i discuteix l'aprenentatge basat en les dinàmiques de co-creació, contribuint a la definició d'una metodologia col·laborativa que promogui el disseny d'un espai públic més sostenible i inclusiu. The city has been the scene of debates in recent decades, the result of growing urbanization and the consequent complexity of urban problems, which impose profound changes in the way of conceiving, producing and managing urban space. Urban laboratories emerge as collaborative learning and experimental environments, exploring new paths for the shared production of knowledge and solutions to current urban problems, promoting collective intelligence based on local experiments tested within a specific culture and situated context. Starting from the idea of an experimental urban laboratory, this research aims to present the first edition of LABTUR, developed in the program TUR: Co-creating Public Spaces, an action-research project carried out in an academic context, in collaboration with the City Council of Cascais, in Portugal. The article presents the results of LABTUR and discusses learning based on co-creation dynamics, contributing to the definition of a collaborative methodology that promotes the design of a more sustainable and inclusive public space.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.287
Teacher spread0.269 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations3
Published2021
Admission routes1
Has abstractyes

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