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Record W2905354725 · doi:10.1111/1468-5973.12253

Move out or dig in? Risk awareness and mobility plans in disaster‐affected communities

2018· article· en· W2905354725 on OpenAlexafffundabout
Timothy J. Haney

Bibliographic record

VenueJournal of Contingencies and Crisis Management · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMount Royal University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWorryFlood mythFlooding (psychology)Risk perceptionPerceptionEnvironmental planningPlace attachmentGeographyPsychologyBusinessSocial psychology

Abstract

fetched live from OpenAlex

Abstract Post‐disaster migration patterns have been thoroughly studied from a demographic standpoint, but affected community residents’ perceptions of ongoing risks and their willingness to remain in an affected community remain under‐researched. Using data generated by 407 surveys and 40 interviews with residents impacted by the 2013 Calgary flood, this study analyses the effects of flood experience on residents’ worry about future floods and their ensuing short‐term and medium‐term mobility plans. The results indicate that home flooding and evacuation orders are both predictive of worry about future floods. In turn, worry about future floods as well as age, homeownership, and place attachment are all predictive of post‐disaster mobility plans. Residents discuss how the flood either strengthened or weakened their place attachment. The paper concludes by discussing the implications for social science research and for public policy that aims to mitigate disaster risk.

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 score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.025
GPT teacher head0.315
Teacher spread0.290 · 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

Citations35
Published2018
Admission routes3
Has abstractyes

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