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Record W4375864187 · doi:10.1080/21683565.2023.2207473

Compromise in the making of urban agroecology: grassroots initiatives and the politics of experimentation in Madrid, Spain

2023· article· en· W4375864187 on OpenAlexafffund
Émilie Houde-Tremblay, Geneviève Cloutier, Nathan McClintock, René Audet, Alain Olivier

Bibliographic record

VenueAgroecology and Sustainable Food Systems · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsUniversité du Québec à MontréalInstitut National de la Recherche ScientifiqueUniversité Laval
FundersMitacs
KeywordsCompromiseGrassrootsAgroecologyPoliticsPolitical scienceEnvironmental planningPublic administrationEconomic growthGeographyEconomicsAgricultureLaw

Abstract

fetched live from OpenAlex

In some cities, food movements are mobilizing around agroecology, using the materiality of their lived environment and their social interactions to put their vision of future food systems into practice. These initiatives unfold in contexts where resources are often limited, forcing trade-offs between activists’ visions of agroecology and what they can do in the immediate future. Drawing on documentary research, 26 semi-structured interviews, and over 100 hours of observation, we examine the evolution of agroecology in Madrid, Spain, focusing on the deployment of three initiatives. We find that to cope with precarity, some activists adapt their practices by adopting scaling-up strategies that involve increasing the number of participants and collaborating with the City of Madrid. However, the political dimension of the simple acts like gardening or procuring agroecological food becomes less clear, raising questions and spurring debate within the social movement. The compromises made and the reflexive stance on what is gained and what is lost, we argue, ultimately are testament and contribute to agroecology’s political vision and goals, albeit in ways that are perhaps quite different from those initially envisioned.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.183

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0000.000

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 head0.242
Teacher spread0.226 · 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.

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

Citations6
Published2023
Admission routes2
Has abstractyes

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