MétaCan
Menu
← Back to cohort
Record W6986683474

Promoting Flood Risk Mitigation Among Canadians Through Effective Risk Communication

2022· dissertation· en· W6986683474 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsRisk communicationFlood mythRisk perceptionPreparednessRisk managementRisk assessmentIT risk managementNatural hazard
DOInot available

Abstract

fetched live from OpenAlex

Amid reports of low levels of flood risk awareness and preparedness among Canadians, risk communication emerges as an important tool for encouraging public participation in flood risk management (FRM) and increasing collective flood resilience. In its most basic form, risk communication informs people of their risk and provides solutions to reduce it. Disaster studies scholars, however, assert that an individual’s decision to take protective action from hazards is mediated by an array of social, economic and cognitive factors. To this end, there are growing calls to incorporate audiences’ social and physical environment as well as insights from behavioural science into risk communication methods to increase their efficacy. \nWhether relevant Canadian stakeholders consider those factors in their flood risk communication strategies is unclear, which raises several fundamental questions about flood risk communication in Canada, such as: What are the challenges and opportunities of incorporating risk perception and risk communication theory into flood risk communication practice? Is flood risk communication by key Canadian communicators (e.g., governments, insurance companies, civil society organizations and academic groups) achieving its goals of increasing the knowledge and capacity for flood preparedness of those at risk? If so, how is this assessed? This research aims to provide a comprehensive review of Canadian municipalities’—important flood risk communicators—flood risk communication practices relative to risk communication theory. Surveys of municipal staff from 18 large, flood-prone Canadian municipalities and interviews of 21 subject matter experts concerning household-level flood risk mitigation were conducted and the results were analysed using risk communication and risk perception literature; the latter is grounded in protection motivation theory, a widely-used behavioural framework in flood risk research. \nThe results indicate that most municipalities’ flood risk communications should theoretically be raising residents’ flood risk awareness and preparedness. Limited time and resources function as the greatest barriers to municipalities’ flood risk communication efforts to the public; such barriers impede some municipalities’ abilities to address known deficiencies in their flood risk communication practices. Public-private and public-public partnerships were identified as critical to overcome municipal resource constraints and to enhance the impact of flood risk education programs and/or flood risk communication messages. \nThe findings have implications for federal and provincial policies to expand local government responsibility for FRM, because they suggest that such decisions neglect the diversity of local governments with respect to their funding and capacities. Future research recommendations include the further application of evaluation frameworks to flood risk communication activities in light of the finding that Canadian flood risk communicators’ metrics of “successful” flood risk communications are highly variable or altogether absent. In the absence of flood risk communication standards, its impact will remain intangible.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0100.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.003
GPT teacher head0.202
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUWSpace (University of Waterloo)→Same topicCell Image Analysis Techniques→French-language works237,207→