Public Participation in the Emergency Response Phase of Flooding: A Case Study of the Red River Basin
Bibliographic record
Abstract
The emergency response phase of flood management is often controversial because of the nature of the decisions that are made, such as the decision to evacuate. The limited literature on public involvement in response efforts indicates that conflict and dissatisfaction often mar emergency response due to the absence of public input. This paper examines the role that the public currently plays in emergency flood response in order to identify how public involvement might be better incorporated into this phase of flood management. The context of the study is the 1997 Red River Flood, the flood of the century, where some dissatisfaction and conflict characterized emergency response efforts. The evaluation is grounded in criteria defined by research participants. The methodology was qualitative and interactive and included semi-structured interviews and a review of documents. The results suggest that members of the public are the first responders to a flood threat, and play a critical role in reducing the damage by undertaking individual and group activities such as moving furniture to upper floors and participating in neighbourhood committees. Their role in provincial and state decisions, such as evacuation, is however, minimal at best. Despite the popular opinion of senior decision-makers, opportunities are identified to involve the public more in both preparation and response through vehicles as simple as a town hall meeting.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".