What comes after crises? Key elements and insights into feedback amplifying community self-organization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".