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Record W4319998156 · doi:10.5751/es-13773-280107

What comes after crises? Key elements and insights into feedback amplifying community self-organization

2023· article· en· W4319998156 on OpenAlexfundvenueno aff
Alice Gallo de Moraes, Juliana Sampaio Farinaci, Deborah Santos Prado, Luciana Araujo, Ana Carolina I. M. Dias, Rafael Ummus, Cristiana Simão Seixas

Bibliographic record

VenueEcology and Society · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoSocial Sciences and Humanities Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsAgency (philosophy)Community organizationCollective actionPublic relationsSocial capitalElement (criminal law)Psychological resilienceBusinessSociologySocial psychologyKnowledge managementPolitical sciencePoliticsPsychologyComputer scienceLaw

Abstract

fetched live from OpenAlex

In face of complex socio-environmental issues experienced in different social-ecological systems, we ask if comprehensive lessons could be learned from cases of community self-organization that were successful in solving collective problems at the local level. Considering that the trajectory of each community is unique and self-organization develops in distinctive settings, we sought to identify the common elements shared by six case studies in Brazil and investigate how they interact (i.e., if they generate feedback that amplifies self-organization), synthesizing the lessons drawn from each case so they may be applied to other contexts. In all cases, community self-organization provided good conditions to overcome crisis and led to desirable changes regarding the problem in question. We explored the underlying mechanisms of successful community self-organization from a social-ecological and community resilience standpoint and identified six elements in common: ability and/or willingness to find opportunities in crisis; partnerships with external actors; human and social capital within the community; generation of income opportunities and/or guarantee of rights; existence of spaces that favor social interaction; and agency oriented to collective mobilization and problem solving. Elements were interconnected and often reinforced one another, generating amplifying feedback, which is seen in the repetition and improvement of practices and attitudes over time and space. Agency was a prominent catalyst for self-organization by generating amplifying feedback that positively affected other elements; it was an element of the feedback chain reinforced by the benefits reaped at the individual level. When collective interests prevailed over individual ones, it was less likely to generate feedback that inhibited self-organization. We argued that ordinary relationships related to different cultural practices and livelihoods were important exercises of collective action that provided communities with a repertoire of responses that could be activated in times of crisis, thus enhancing their capacity to self-organize.

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)0.006
metaresearch head score (Gemma)0.016
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 score0.007
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.015
Scholarly communication0.0070.007
Open science0.0020.007
Research integrity0.0020.002
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 head0.051
GPT teacher head0.386
Teacher spread0.334 · 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
Published2023
Admission routes2
Has abstractyes

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