FLOOD-RISK COMMUNICATION: INSIGHTS FROM CANADIAN HOUSEHOLDS
Bibliographic record
Abstract
This research investigates flood-risk communication challenges in Canada, emphasizing the need for tailored strategies to address diverse household preferences and values. In Chapter 2, we examine Canadian household experiences with flood-risk information, aiming to identify new communication needs and bridge the gap between households and flood-risk managers. The interviews reveal previously overlooked flood risk information needs. The importance of tailored communication strategies was highlighted by household participants as they emphasized the need for information that caters to their unique circumstances and requirements. Moreover, fairness emerged as a crucial aspect of flood-risk communication, prompting a call for equitable practices to address vulnerabilities affecting specific households. In Chapter 3, we investigate household values and preferences on flood-risk information through a survey of at-risk households in Canada, uncovering diverse preferences, values and needs for tailored risk information. Additionally, significant differences in flood-risk knowledge, accessibility, and transparency are observed among risk-status groups, with higher awareness among those who perceive themselves at risk. Overall, this research emphasizes the importance of understanding diverse values and preferences within households regarding flood-risk information. Strengthening flood-risk communication strategies and addressing information gaps can lead to more informed risk perceptions and improve awareness among at-risk households in Canada
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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 | 0.027 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".