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Record W4410300207 · doi:10.1080/17477891.2025.2503915

Flood risk management policies and conceptualizations of resilience in Vancouver, Canada

2025· article· en· W4410300207 on OpenAlexafffundabout
Jacob Ventura, Greg Oulahen, Daniel Henstra, Jason Thistlethwaite

Bibliographic record

VenueEnvironmental Hazards · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
FundersMarine Environmental Observation Prediction and Response Network
KeywordsResilience (materials science)Flood risk managementFlood mythRisk managementEnvironmental planningEnvironmental resource managementEmergency managementPolitical scienceGeographyBusinessEnvironmental scienceArchaeology

Abstract

fetched live from OpenAlex

Resilience has become a central objective of flood risk management (FRM). In Vancouver, Canada, decision makers and stakeholders are contending with evolving flood risks and implementing policies to address them. These policies can be characterised based on four distinct strategies: protect, accommodate, avoid, and retreat. Through a systematic analysis of plans and policy documents, this study investigates whether past and current FRM measures increase resilience according to three unique conceptualizations: resistance, recovery, and creative transformation. The analysis indicates that the protect and accommodate approaches have dominated Vancouver's FRM policies while the avoid and retreat options are much less used due to clashing priorities. As a result, the city's FRM policies appear to increase resilience when understood as resistance and recovery, but do not meet the threshold of creative transformation.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0080.009
Scholarly communication0.0060.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.196
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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2025
Admission routes3
Has abstractyes

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