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Record W3116553706 · doi:10.29122/jstmb.v15i2.4502

REGIONAL PLANNING AND DEVELOPMENT BASED ON DISASTER RISK REDUCTION IN BANTEN PROVINCE

2020· article· en· W3116553706 on OpenAlexaff
Novian Andri Akhirianto

Bibliographic record

VenueJurnal Sains dan Teknologi Mitigasi Bencana · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsDisaster risk reductionTypologyVulnerability (computing)GeographyEnvironmental planningUnit (ring theory)HazardBusinessComputer science

Abstract

fetched live from OpenAlex

Abstract Banten Province is one of the areas prone to disaster, because it has various hazards of disaster. On the other side, the process of regional development continues as well as all human activities. To handling these problems, disaster risk reduction efforts are needed by taking into account the regional developments. The purposes of this research are to identify the level of disaster risk, the level of regional development, and to find out the relationship between disaster risk and the level of regional development in Banten Province. This research was conducted using the literature study method, by searching and studying various literatures. Data analysis was performed using scoring techniques and an integrated model of the relationship between regional development and disaster risk, with the unit of analysis is district/ city. The results showed that there were 2 typologies of the relationship between disaster risk and regional development in Banten Province, 5 districts/ cities (Pandeglang Regency, Lebak Regency, Tangerang Regency, Serang Regency and Tangerang City) in typology I (high) and 3 districts/ cities (Cilegon City, Serang City, and Tangerang Selatan City) in typology III (low). Keywords: hazard, vulnerability, capacity, disaster risk, regional development, banten province.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score0.521

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.277
Teacher spread0.239 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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