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Record W2995423018 · doi:10.3126/japfcsc.v1i1.26707

Gorkha Earthquake 2015 and Post Disaster Reconstruction in Nepal: Challenges and Prospects

2018· article· en· W2995423018 on OpenAlexaff
Rajesh Uprety

Bibliographic record

VenueJournal of APF Command and Staff College · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsThe Alberta Paraplegic Foundation
Fundersnot available
KeywordsAftershockLivelihoodDisaster areaPolitical scienceResilience (materials science)GeographyEconomic growthBusinessSeismologyGeologyArchaeologyEconomics

Abstract

fetched live from OpenAlex

The Gorkha earthquake of moment magnitude 7.6 hit the central region of Nepal on April 25, 2015; with the epicenter about 77 km northwest of Kathmandu Valley. This paper aims to explore the challenges and opportunities of reconstruction in earthquake punched areas of Nepal. The Gorkha earthquake on April 25, 2015, has significantly affected the livelihood of people and overall economy in Nepal, causing severe damage and destruction in central Nepal including nation’s capital. A larger part of the earthquake affected area is difficult to access with rough terrain and scattered settlements, which posed unique challenges and efforts on a massive scale reconstruction and rehabilitation. Challenge of reconstruction of thousands houses is tough for Nepal in the background of its uproar political scenario and weak governance. With significant actors involved in the reconstruction process, no appreciable relief has reached to the ground, which is reflected over the frustration of affected people. Although the earthquake negatively influenced the country’s economy, it opened the opportunity to create sustainable economic developments through proper disaster mainstreaming like construction of earthquake resilience infrastructures, new education and training, media-based mass awareness, and coordinated actions in different parts of the society. Disaster is also an opportunity for development. Hence, if we start thinking for the opportunities after the disaster in a constructive way, still there is a flourishing future of development. This paper is prepared by analyzing few literatures and the personal experiences of the author being as a part of rescue and relief operation in Gorkha during the critical flash of earthquake.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.479

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.031
GPT teacher head0.249
Teacher spread0.218 · 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 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

Citations2
Published2018
Admission routes1
Has abstractyes

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