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Record W2755414055 · doi:10.1193/121716eqs242m

Implementing Nepal's National Building Code: A Case Study in Patience and Persistence

2017· article· en· W2755414055 on OpenAlexaff
Lucy A. Arendt, Ayse Hortacsu, Kishor Jaiswal, John Bevington, Surya Narayan Shrestha, F. C. Lanning, Garmalia Mentor-William, Ghazala Naeem, Kate Thibert

Bibliographic record

VenueEarthquake Spectra · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsRead Jones Christoffersen (Canada)
Fundersnot available
KeywordsBuilding codePatienceCode (set theory)BusinessCivil engineeringForensic engineeringGeographyEnvironmental planningEngineeringComputer sciencePsychology

Abstract

fetched live from OpenAlex

The April 2015 Gorkha earthquake in Nepal revealed the relative effectiveness of the Nepal Standard or the national building code (NBC), and irregular compliance with it in different parts of Nepal. Much of the damage to more than half a million residential structures in Nepal may be attributed to the prevalence of owner‐built or owner‐supervised construction and the lack of owner and builder responsiveness to seismic risk and training in the appropriate means of complying with the NBC. To explain these circumstances, we review the protracted implementation of the NBC and the role played by one organization, the National Society for Earthquake Technology—Nepal (NSET), in the implementation of the NBC. We also share observations on building code compliance made by individuals in Nepal participating in workshops led by the Earthquake Engineering Research Institute's 2014 class of Housner Fellows.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0110.005
Scholarly communication0.0040.003
Open science0.0020.006
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.058
GPT teacher head0.361
Teacher spread0.303 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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