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Record W3022122499 · doi:10.33701/jipwp.v45i2.692

PENYUSUNAN RENCANA TATA RUANG WILAYAH (RTRW) PERTAHANAN PADA IBU KOTA BARU REPUBLIK INDONESIA

2019· article· id· W3022122499 on OpenAlexaff
Agus Subagyo, Udaya Madjid

Bibliographic record

VenueJurnal Ilmu Pemerintahan Widya Praja · 2019
Typearticle
Languageid
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsEncana (Canada)WiLAN (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsArt

Abstract

fetched live from OpenAlex

Penelitian ini menganalisis tentang Rencana Tata Ruang Wilayah (RTRW) Pertahanan Negara pada Ibu Kota Baru Republik Indonesia, yang seharusnya dirancang melalui dua pendekatan sekaligus, yakni pendekatan kesejahteraan (prosperity approach) dan pendekatan keamanan (security approach), sehingga akan mampu menjadi ibu kota yang smart and green city sekaligus juga menjadi secure and defence city, dalam menghadapi ancaman, baik ancaman militer maupun ancaman nir militer. Dengan menggunakan metode kualitatif dan teknik pengumpulan data berupa observasi, wawancara, dan studi literatur / penelaahan dokumentasi, disimpulkan bahwa diperlukan road map RTRW pertahanan negara pada ibu kota Republik Indonesia yang dirancang secara terpadu, terintegrasi, terpola, dan berkelanjutan.

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)0.000
metaresearch head score (Gemma)0.000
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: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0210.004

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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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