Risk-Reduction, Coping, and Adaptation to Flood Hazards in Manitoba, Canada: Evidence from Communities in the Red River Valley
Bibliographic record
Abstract
In general, much is known about patterns relating to flood risk reduction, coping, and adaptation in various types of communities; however, knowledge of their drivers—which are critical for building community resilience to natural hazards—is limited. The present study investigates the influencing factors of coping and adaptation measures vis-a-vis flood hazards at the community level and examines their interrelationships. This work employs a “case study” approach and analyzes two towns—St. Adolphe and Ste. Agathe—in the Red River Valley in the province of Manitoba, Canada. Data collection consisted of in-depth interviews with key informants and obtaining oral histories from the locals, along with an examination of secondary official records and documents. The results revealed that the major drivers of local-level coping and adaptation include functioning partnerships among stakeholders, strong institutional structures that facilitate interactive learning, knowledge co-production, resources sharing, communication and information sharing, and infrastructure supports. It was observed that an institutional atmosphere conducive to spontaneous network development yields diverse coping and adaptation strategies. To improve the outcomes of coping and adaptation measures, close collaboration between community-based groups and formal and quasi-formal institutions, and transparency in decision-making processes are vital.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".