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Record W4412077809 · doi:10.22487/peweka.v4i1.52

Perencanaan Struktur dan Pola Ruang Kawasan Pemerintahan Tobadak berbasis Konsep Urban Resilience

2025· article· en· W4412077809 on OpenAlexaff
Muhammad Irfan, Sri Wahyuni, I Kamaruddin

Bibliographic record

VenueJurnal PeWeKa Tadulako · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Governance and Development
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsResilience (materials science)Physics

Abstract

fetched live from OpenAlex

Central Mamuju Regency is currently aggressively carrying out sustainable regional development. The development aims at equitable distribution in the economic, social and environmental fields. As with developments in developing regions, the very rapid penetration of development always creates an urban paradox which in this case triggers a process of urbanization of the population from rural areas to cities. This planning aims to create a space capable of meeting and controlling the needs of the Tobadak Government Area both economically, socially and environmentally. The analysis techniques used include: SWOT analysis, AHP analysis, Spatial analysis and Space Syntax analysis. The data used are primary and secondary data obtained from government agencies as well as observation, documentation and interviews. The main results of this planning are the master plan, spatial structure and pattern planning as well as regional disaster mitigation based on the concept of sustainable and integrated development.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.010
GPT teacher head0.291
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 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
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
Published2025
Admission routes1
Has abstractyes

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