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Record W4313505682 · doi:10.1177/028072701603400302

School Construction as Catalysts for Community Change: Evidence from Safer School Construction Projects in Nepal

2016· article· en· W4313505682 on OpenAlexaff
Rebekah Paci-Green, Bishnu Pandey

Bibliographic record

VenueInternational Journal of Mass Emergencies & Disasters · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsSAFERIntervention (counseling)Psychological interventionCommunity engagementMasonryBuilt environmentSocial capitalPublic relationsEngineeringBusinessPolitical scienceCivil engineeringPsychologyComputer securityComputer science

Abstract

fetched live from OpenAlex

Organizations in Nepal have retrofitted weak school buildings using earthquake-resistant construction techniques for over a decade. Some of these safer school projects have been carried out as technical interventions only, while others have been embedded within programs of community engagement, masonry training, and oversight. Following the 2015 Gorkha earthquake, 12 school sites were assessed through visual inspection and a series of community interviews to understand the impacts of safer school construction projects on local perceptions and construction practice. Compared to communities that had received technical intervention only, or no intervention at all, communities that had experienced community engagement were more knowledgeable of earthquake-resistant construction techniques and reported more adoption of these techniques in housing construction. They also evidenced more trust in the school building, using it as shelter following the earthquake. Community engagement can amplify the benefit of future school retrofit and reconstruction projects, simultaneously building social and infrastructure capital.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.069
GPT teacher head0.351
Teacher spread0.281 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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