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Record W4392910638 · doi:10.32920/25413853.v1

The Interaction of Resilience Concepts and Flood Adaptation Strategies in Vancouver, British Columbia

2024· preprint· en· W4392910638 on OpenAlexaffabout
Jacob Ventura

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsFlood mythResilience (materials science)Adaptation (eye)Psychological resilienceResistance (ecology)PerceptionEnvironmental resource managementEnvironmental planningPolitical scienceGeographyPsychologySocial psychologyEconomicsEcology

Abstract

fetched live from OpenAlex

This study explores the interaction between adaptation, resilience, resident perceptions of risk management, and flood policy in Vancouver, British Columbia. A systematic policy analysis determines how four adaptation strategies (protect, accommodate, retreat, and avoid) are employed in the municipality and interprets the degree to which current flood risk reduction efforts are capable of enhancing resilience across three conceptualizations established in literature (resistance, recovery, and creative transformation). Survey analysis of how residents understand and prioritize the concept of resilience is then applied to policy analysis findings to reveal a misalignment of policy targets and expectations. Findings indicate that protect and accommodate strategies dominate policy to compensate for ongoing floodplain development while the retreat and avoid strategies are ignored despite their utility in addressing exposure. Such policy fails to align with resident perceptions that resilience defined by creative transformation should be the objective of flood adaptation efforts.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.281

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.003
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.009
GPT teacher head0.264
Teacher spread0.255 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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