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Record W2137046084 · doi:10.22146/ijg.2398

COMMUNITY- BASED APPROACH TO REDUCE EARTHQUAKE VULNERABILITY IN KATHMANDU VALLEY

2012· article· en· W2137046084 on OpenAlexaff
Punya Sagar Marahatta

Bibliographic record

VenueIndonesian Journal of Biotechnology (Universitas Gadjah Mada) · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHuman settlementVulnerability (computing)BusinessEnvironmental planningResilience (materials science)Citizen journalismCommunity resilienceRisk managementCapital cityEmergency managementCorporate governanceEnvironmental resource managementEconomic growthResource (disambiguation)GeographyFinancePolitical scienceComputer securityEconomicsComputer science

Abstract

fetched live from OpenAlex

Nepal is a vulnerable country in terms of multiple disasters and one of them is earthquakes.Disaster risk management experts believe that one of the ways to reduce the vulnerabilities is by adopting a community-based disaster risk management approach.Unfortunately, Nepal has limited resources; the culture of insurance against disasters does not currently exist.This paper describes the findings of research conducted in traditional settlements of Kathmandu Valley multiple case studies, household surveys, and community-based participatory research have identified that the culture of participation in local development activities and fundraising at the local level could contribute to disaster risk management in traditional settlements of Kathmandu Valley.This paper thus suggests developing resilience governance at the community level through consumer cooperatives in order to reduce vulnerability to earthquakes and capitalize on already existing financial, human, and social capital and resources.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.440
Threshold uncertainty score0.637

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.282
Teacher spread0.247 · 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.

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

Citations3
Published2012
Admission routes1
Has abstractyes

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