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Record W4414111914 · doi:10.1177/02637758251361706

Commoning, heterotopia, and transformation: An analytical framework for and from contested spaces

2025· article· en· W4414111914 on OpenAlexafffundabout
Amy R. Poteete, Pavel Kunysz, Nik Luka

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2025
Typearticle
Languageen
FieldEngineering
TopicUrban Design and Spatial Analysis
Canadian institutionsMcGill UniversityConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaUniversité de LiègeFonds de recherche du QuébecConcordia UniversityFonds De La Recherche Scientifique - FNRSMcGill University
KeywordsHegemonyCapitalismTransformative learningCommonsOpposition (politics)Heterotopia (medicine)Norm (philosophy)

Abstract

fetched live from OpenAlex

Commoning occurs when people recognize that they share something and develop a sense of mutuality toward each other along with a shared responsibility for whatever they share. Because sharing and mutuality contrast with the individualism, competitiveness, and profit orientation of contemporary capitalist societies, commoning is widely heralded for its transformative potential. Nonetheless, commoning is not inherently transformative. We argue that whether commoning supports transformation depends on its relationship with heterotopic processes. Both commoning and heterotopia-ideal typical "other" spaces characterized by looseness and denormalization-present alternatives to hegemonic norms, especially those of state-centricity, hierarchical social organization, and the prioritization of market relationships and economic growth, but they are distinct processes that do not necessarily coincide. We propose an analytical framework to guide analysis of the relationship between commoning and heterotopia and illustrate it with examples from contested urban green spaces in Liège (Belgium), Montréal (Canada), and Brussels (Belgium).

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0100.066
Scholarly communication0.0140.016
Open science0.0030.010
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.231
Teacher spread0.215 · 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 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

Citations1
Published2025
Admission routes3
Has abstractyes

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