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Record W2752569977 · doi:10.1080/14606925.2017.1352832

Infrastructuring Place. Citizen-led Placemaking and the Commons

2017· article· en· W2752569977 on OpenAlexafffund
Maria Frangos, Thomas D. Garvey, Irena Knežević

Bibliographic record

VenueThe Design Journal · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPlacemakingCommonsPublic relationsCitizen journalismContext (archaeology)Corporate governancePeer productionNarrativeSociologyPolitical scienceParticipatory designUrban planningKnowledge managementBusinessEngineeringGeographyUrban designCivil engineeringComputer science

Abstract

fetched live from OpenAlex

A proliferation of participatory spatial practices is emerging in cities, as citizens seek alternative forms of urban governance and land use. Characterised by peer-to-peer production and mediated through the use of digital technologies, these practices are part of a larger narrative about the commons, the ways in which citizens participate in them, and the ways in which knowledge is produced and shared. This case-study uses participatory mapping, group interviews and document analysis to explore how participants in two urban gardening projects make place, and probes whether their practices offer new understandings of community-led placemaking. Results emphasise the important role citizens play in creating, designing and maintaining the commons and demonstrate the ways in which individuals and community groups working outside of a professional urban design context expand placemaking into a vehicle for social change.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.024
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.020
GPT teacher head0.214
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations10
Published2017
Admission routes2
Has abstractyes

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