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Record W2622661508 · doi:10.1002/jcop.21897

Place-based loss and resilience among disaster-affected youth

2017· article· en· W2622661508 on OpenAlexafffundabout
Leila Scannell, Robin S. Cox, Sarah Fletcher

Bibliographic record

VenueJournal of Community Psychology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsRoyal Roads University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVulnerability (computing)Psychological resilienceFlood mythPlace attachmentResilience (materials science)PsychologySense of placeRelevance (law)Social psychologySociologyGeographyPolitical scienceComputer securitySocial science

Abstract

fetched live from OpenAlex

As research on young people's disaster experiences is accumulating, one important yet understudied factor underlying their vulnerability and resilience is their connection to certain places. Youth affected by the 2013 floods in Southern Alberta, Canada, provided photographs of places important to their flood experiences and engaged in peer-to-peer interviews to discuss place loss and place-based strength. Damaged or changed places disrupted youth's reliance on place for activities, resources, social ties, sense of continuity, and a connection to the past. Places provided strength when they offered escape from the postdisaster chaos, enabled youth to contribute to recovery, supported physical and psychological need satisfaction, and symbolized strength, renewal, or hope. These findings demonstrate the relevance of place to youth's disaster experiences and inform future qualitative and quantitative work in this area.

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)0.002
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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.383
Teacher spread0.332 · 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

Citations31
Published2017
Admission routes3
Has abstractyes

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