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Record W2524781129 · doi:10.1080/10225706.2016.1235053

The political economy of 2015 Nepal earthquake: some critical reflections

2016· article· en· W2524781129 on OpenAlexaff
Kapil Dev Regmi

Bibliographic record

VenueAsian Geographer · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCitizen journalismGovernment (linguistics)AftershockPopulationDebtPolitical scienceInternational communityPoliticsLoanEconomic growthSustainable developmentDevelopment economicsPolitical economyFinanceBusinessSociologyEconomicsEngineeringLawCivil engineering

Abstract

fetched live from OpenAlex

A massive earthquake of 7.6 magnitudes on 25 April 2015 and a major aftershock of 6.8 magnitudes on 12 May 2015 hit central Nepal. The earthquake took the lives of about 9000 people, injured about 24,000 and affected one-third of Nepal’s total population (28 million). Despite a huge amount of money (US$ 4.4 billion) pledged by the international community, reconstruction works could not take place on time. Using participatory approach to reconstruction and development as a theoretical framework and reflexivity as a methodological tool, this paper argues that the delay in reconstruction was caused by the inability of the Government of Nepal (GON) as well as the international community, mainly donors, to encourage local participation. The amount of loan pledged by the international community has increased Nepal’s debt stock rather than really helping those who are affected by the disaster. The paper concludes that the modernist top-down model of development – that both government and donors take for granted – has created roadblocks towards understanding Nepal’s contextual realities. Sustainable reconstruction and development cannot be achieved without strengthening the capability of local communities.

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)0.000
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.344
Teacher spread0.326 · 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 designTheoretical or conceptual
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

Citations29
Published2016
Admission routes1
Has abstractyes

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