Social learning and collective action in flood-hazard management in Manitoba, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it