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Record W3200244258 · doi:10.1111/disa.12510

Women's participation in disaster recovery after the 2005 Kashmir, Pakistan earthquake

2021· article· en· W3200244258 on OpenAlexaff
Shehla Gul, Tara K. McGee

Bibliographic record

VenueDisasters · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFocus groupVocational educationSocioeconomicsAgricultureOccupational safety and healthSuicide preventionPublic healthBusinessPoison controlEngineeringEconomic growthEnvironmental healthPolitical scienceGeographyPsychologyMedicineSociologyNursingMarketingEconomics

Abstract

fetched live from OpenAlex

This study sought to learn how women participated in the recovery process after the Kashmir earthquake of October 2005 in Union Council Langarpura, Azad Kashmir state of Pakistan. Focus-group discussions, semi-structured interviews, and participant observations were conducted with a total of 48 participants. The results revealed that women played various important roles in the reproductive, productive, and community spheres, encompassing, inter alia, normal household responsibilities of cooking, cleaning, and caring for cattle, and non-traditional tasks such as rebuilding the home. In addition, they participated in income-generating activities such as carrying construction materials and water for daily wages, dairy farming, and working in fields and in the education and health sectors. Community endeavours, meanwhile, consisted of search and rescue, caring for the injured, collective cooking and food sharing, and supervising the reconstruction of public structures, including schools, roads, and water supply facilities, and establishing a sewing centre to provide vocational training to local women.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.032
GPT teacher head0.384
Teacher spread0.351 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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