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Record W3183735468 · doi:10.20961/shes.v3i1.45020

Active Role of Women in Disaster Prone Areas

2020· article· en· W3183735468 on OpenAlexaff
Ratnanik Ratnanik, Yulinda Erma Suryani

Bibliographic record

VenueSocial Humanities and Educational Studies (SHEs) Conference Series · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsVulnerability (computing)Context (archaeology)GeographyParagraphSocioeconomicsEmergency managementTributaryPolitical scienceEconomic growthEnvironmental planningSociologyComputer securityArchaeologyCartographyLaw

Abstract

fetched live from OpenAlex

Indonesia is a country that has two (2) seasons, namely the rainy season and the dry season. Klaten Regency is close to Mount Merapi, east of the Klaten area close to the Dengkeng River and its tributaries. Disaster management law No. 4 of 2007 article 4 paragraph c states that ensuring the implementation of disaster management in a planned, integrated, coordinated and comprehensive manner and respecting local culture. This study discusses the higher vulnerability of women compared to men, so that it requires handling that requires more active roles of women residents or volunteers. The aim of this research is to see how active women's roles are in disaster disasters and what roles women play in the context of disasters. From the results of the study it can be concluded that the active role of women in disaster-prone areas in Klaten Regency has begun to be trained. Evidenced by joining the Disaster Risk Reduction Organization (OPRB) in Klaten

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.065
GPT teacher head0.310
Teacher spread0.246 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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