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Record W4388566147 · doi:10.18280/ijsse.130518

The Urgency of Establishing a Natural Disaster Management Agency in Indonesia

2023· article· en· W4388566147 on OpenAlexvenueno aff
Ellectrananda Anugerah Ash-shidiqqi, Aidul Fitriciada Azhari, Kelik Wardiono, Wardah Yuspin

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
FundersUniversitas Muhammadiyah Surakarta
KeywordsAgency (philosophy)Emergency managementNatural disasterEnvironmental planningOccupational safety and healthBusinessPoison controlNatural (archaeology)Medical emergencyEnvironmental resource managementGeographyEnvironmental scienceMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

Severe damage and large losses will be the result of the earthquake.Thus, pre-disaster activities to reduce the impact of carried out by various parties such as the central and local governments, relevant agencies and society is very important.Therefore, the central government will make a design "action against earthquakes" as a master plan for dealing with disasters earthquakes, including pre-disaster measures, emergency response, and postdisaster stages.To measure the impact of these disaster risk reduction efforts, the central government will set one goal that can be measured quantitatively for a certain period of time.Observations of the achievements of these efforts will be continued on a regular basis.Moreover, participation and coordination with local government is very important to achieve this goal, so the local government concerned must take responsibility for responsible for developing regional goals that are in line with the coping strategy national disaster.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.006
GPT teacher head0.194
Teacher spread0.189 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2023
Admission routes1
Has abstractyes

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