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Record W3120309073 · doi:10.3126/dsaj.v14i0.29759

Citizens’ Reflection on Democracy and Disaster in Nepal in the Wake of the 2015 Earthquake

2020· article· en· W3120309073 on OpenAlexaff
Isha Sharma

Bibliographic record

VenueDhaulagiri Journal of Sociology and Anthropology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMount Royal University
Fundersnot available
KeywordsWakeReflection (computer programming)DemocracyPolitical scienceSeismologyGeologyPoliticsLawEngineeringComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

On April 25, 2015, Nepal was hit by a massive earthquake. Thousands of lives were lost. Extensive damage to infrastructure and property was reported. Using 30 interviews, I firstly examine how the people survived in the early days of the disaster. Secondly, I discuss how the citizens of Nepal, perceived democracy as a political system that is still novel for them, in the aftermath of the crisis. The interviewees reflected on the government’s response to the earthquake. Evidently, the study highlights the disjuncture between the kinds of relief a democratic state is expected to provide for the citizens and the state’s actual response to the needs of the earthquake survivors. Nepal has adopted democracy since 1990, however, it has failed to deliver on its promises, and people are thus ambivalent about the system. However, in the final analysis, it becomes apparent that people are unwilling to revert back to the old autocratic system. The conclusions of the study compel one to consider certain social processes. What affects citizens’ expectations of their government in the aftermath of a major disaster is contingent upon how states have acted in normal times. The state’s response to disasters might be influenced by what citizens expect from the state in the first place, thus, leading to a self-fulfilling prophecy. Finally, a democratic society is preferred by most, and the only way for the government to be more robust is to compel the leaders to adhere to the laws and regulations and operate according: those who break the laws must be made accountable.

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)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.012
Scholarly communication0.0070.005
Open science0.0010.009
Research integrity0.0040.009
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 head0.039
GPT teacher head0.355
Teacher spread0.317 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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