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Political engagement in urban agriculture: power to act in community gardens of São Paulo

2022· article· en· W4225789357 on OpenAlexaboutno aff
André Ruoppolo Biazoti, Marcos Sorrentino

Bibliographic record

VenueAmbiente & sociedade · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsnot available
FundersUniversidade de São PauloFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCommonsEthnographyPoliticsPower (physics)SociologyCollective actionInstitutionWork (physics)Participant observationPolitical actionCommunity engagementPublic administrationPolitical sciencePublic relationsSocial scienceEngineeringLawAnthropology

Abstract

fetched live from OpenAlex

Abstract In the municipality of São Paulo, the institution of community gardens has led an innovative social process of self-management of public spaces by citizens, using social networks to organize interventions in an activist way. The research sought to investigate political engagement in community gardens in São Paulo, bringing a perspective based on the concepts of power to act, present in Espinosa’s philosophy, and of commons, based on the work of Dardot and Laval (2017). The methodology consists of an ethnography based on semi-structured interviews and participant observation in community garden joint-efforts, meetings and events. In these meetings, it is possible to consider that there are affections empowering the subjects to engage in significant changes for territorial management, enabling the formation of an expanded collective of action that establishes unique forms of management confronting institutional powers.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.007
Scholarly communication0.0020.001
Open science0.0000.004
Research integrity0.0010.001
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 head0.017
GPT teacher head0.235
Teacher spread0.218 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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