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Record W4313505641 · doi:10.1177/028072701803600202

Paradise Found? the Emergence of Social Capital, Place Attachment, and Civic Engagement after Disaster

2018· article· en· W4313505641 on OpenAlexaffabout
Timothy J. Haney

Bibliographic record

VenueInternational Journal of Mass Emergencies & Disasters · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMount Royal University
Fundersnot available
KeywordsPlace attachmentSocial capitalFlood mythCivic engagementScholarshipSocial engagementSociologyPolitical scienceGeographyPublic relationsSocial psychologySocial sciencePsychologyPolitics

Abstract

fetched live from OpenAlex

Social science research on disaster-affected communities uses social capital to explain a variety of post-disaster outcomes. A promising recent line of inquiry looks at how disasters generate new forms of social capital, and reinvigorate place-based social networks and place attachment. Using survey data collected from 407 Calgary residents affected by the catastrophic 2013 Southern Alberta Flood, as well as interview data from 40 residents, this article examines factors that contributed to residents’ expansion of their social networks during the disaster, and the impact of expanded social networks on residents’ post-disaster place attachment and civic engagement. Findings reveal that people most affected by the flood, i.e., those who experienced house flooding and longer evacuations, were most likely to make new contacts during the disaster and immediately after it. However, results also indicate that these new forms of social capital did not translate into greater place attachment, even though they did engender some post-flood civic engagement. Overall, inundation, evacuation, and displacement are predictive of lesser post-disaster place attachment. The article concludes by discussing the relevance of the findings for theory and disaster scholarship.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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.

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

Citations22
Published2018
Admission routes2
Has abstractyes

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