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Record W2136099755 · doi:10.1068/a43238

Flood Perception and Mitigation: The Role of Severity, Agency, and Experience in the Purchase of Flood Protection, and the Communication of Flood Information

2010· article· en· W2136099755 on OpenAlexfundno aff
Emma Soane, Iljana Schubert, Peter Challenor, Rebecca J. Lunn, Sunitha Narendran, Simon Pollard

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's UniversityUniversity of GlasgowNatural Environment Research CouncilSight Research UK
KeywordsSeriousnessFlood mythFlooding (psychology)Agency (philosophy)BusinessPerceptionFlood mitigationEnvironmental planningPsychologyGeographyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

Protection of human life and property from flooding is a strategic priority in the UK. We examine how to encourage home owners to protect themselves and their residences. A model of factors that influence the decision to buy flood-protection devices is tested using survey data from 2109 home owners. The results show that the majority of respondents have not purchased domestic flood protection ( N = 1732; 82.1%). Purchase of flood-protection devices was influenced by age; perceived seriousness; and beliefs about, and trust in, the role of regulators in managing flooding. In younger respondents the perceived seriousness of the dangers of flooding acted as precursors and barriers to action depending on individual sense of responsibility and agency. The second part of the study examines responsiveness to information. Information about flooding alone was insufficient to promote behavioural change, particularly among people who had not experienced a flood or who believed that they were not in a flood zone. Implications for understanding flood protection, managing agency issues, and flood-communication campaigns are discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.199
Teacher spread0.194 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations58
Published2010
Admission routes1
Has abstractyes

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