Local level flood management, risk reduction, and coping and adapting in the Red River Valley, Manitoba, Canada
Bibliographic record
Abstract
The purpose of this research was to study the pattern of flood preparedness, response and recovery, and the drivers of changes in flood management, i.e., coping and adapting, in the Red River Valley of Manitoba, Canada. I conducted my research following a case study approach with a qualitative research design. My study included the communities of St. Adolphe and Ste. Agathe in the Rural Municipality (RM) of Ritchot in Southern Manitoba. Techniques and instruments that were applied for data collection included Key Informant Interviews (8), Oral History Interviews (7), and Document Review. The findings of the research revealed that local community-level flood preparedness, response, and recovery in the Province of Manitoba are primarily designed, governed, managed, and evaluated by Provincial government authorities using a top-down approach. Given that Canada has a long history of bailing out disaster victims, and as the approach has been generally non-participatory, community members show reluctance in taking precautionary measures, resulting in undesired losses and damages. The findings of my research also identified the major drivers of coping and adaptation measures for building flood resilience within the communities, which included: functioning partnerships among stakeholders, strong institutional structures that facilitate interactive learning, knowledge co-production, resource sharing, communication and information sharing, and infrastructure supports. However, there were only a few efforts to develop an institutional atmosphere conducive to spontaneous network development, yielding diverse coping and adaption strategies at the community level in the Province of Manitoba.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".