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Record W4297794746 · doi:10.1080/17477891.2022.2095970

Managed retreat from high-risk flood areas: exploring public attitudes and expectations about property buyouts

2022· article· en· W4297794746 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnvironmental Hazards · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of Waterloo
FundersPartners Healthcare
KeywordsFlood mythBusinessIncentiveRisk managementFlood risk managementFinanceMarketingEconomicsGeography

Abstract

fetched live from OpenAlex

Increasing flood risk requires governments to develop innovative solutions for flood risk management. The effectiveness of these solutions depends, in part, on their social acceptability. This paper presents the findings of a national survey to explore the social acceptability of property buyouts as a form of managed retreat from flood risk in Canada. It discusses public attitudes and expectations towards property buyout programmes in high-risk flood zones, including their salience, essential design elements, and factors that would influence household acceptance of a property buyout offer. The results show there is an appetite for property buyout programmes to reduce flood risk in high-risk zones. Moreover, the social acceptability of such programmes is highest when participation is voluntary, flexible pricing options are combined with financial incentives, and programme design and implementation are transparent. Participants indicated costs for these programmes should be borne primarily by governments and shared between governments at different levels. The findings suggest that although property buyouts—and managed retreat more generally—are considered a socially acceptable approach to flood risk management, their efficacy will depend on programme design, stakeholder collaboration, and effective communication of risk to vulnerable populations. Policy recommendations are discussed in response to these findings.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.015
GPT teacher head0.204
Teacher spread0.190 · 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