Social learning and collective action in flood-hazard management in Manitoba, Canada
Bibliographic record
Abstract
Knowledge of social learning about flood hazards in the literature, especially regarding its transformation into collective action for risk reduction, is very limited. This study addresses these gaps by developing an integrated framework that describes how social learning is transformed into collective action – particularly the underlying components and processes – and then applying it to empirical case studies of two communities, (namely, St. Adolphe and St. Agathe) of the Rural Municipality of Ritchot, Manitoba, Canada. Primary data were collected during the summer months of 2022 using participatory research appraisal (PRA) tools (i.e. key informant interviews and oral histories), while secondary data were collected primarily via government and NGO sources. The findings revealed that a) the flood experience and related interactions among community members and local institutions produced unique and distinct types of social learning, and b) that local and multilevel institutions had helped to create learning platforms that facilitated the formation of strategies for collective action related to flood risk reduction. These processes resulted in single – and double-loop learning at the community level. Based on the findings of this work, we recommend that learning and reflection relating to community members’ flood experiences be integrated into disaster risk reduction and management policies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".