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Record W3195081215 · doi:10.36859/jap.v4i2.629

STRATEGI PEMBANGUNAN INDUSTRI PERTAHANAN PADA NEGARA KEPULAUAN GUNA MENDUKUNG PERTAHANAN NEGARA

2021· article· id· W3195081215 on OpenAlexaff
Dede Rusdiana, Ali Yusuf, Suyono Thamrin, Resmanto Widodo

Bibliographic record

VenueJurnal Academia Praja · 2021
Typearticle
Languageid
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Indonesia merupakan negara kepulauan yang memiliki banyak potensi sumber daya. Potensi sumber daya nasional dapat digunakan untuk pembangunan ekonomi, salah satunya pembangunan industri pertahanan. Pembangunan industri pertahanan bukan hanya untuk kebutuhan alat pertahanan namun, juga dapat membantu masyarakat dalam roda perekonomian. Dalam mencapai tujuan negara optimalisasi negara kepulauan maka, perlu adanya perbaikan sistem yang mengarah pada kebijakan, dimana dalam penataan kebijakan diperlukan tahapan manjemen yaitu perencanaan (plan), pelaksanaan (do /action), dan penilaian hasil (evaluate). Kebijakan dilaksankan dengan menggunakan sumber daya nasional Permasalahan yang dihadapi untuk pembangunan industri pertahanan adalah belum optimalnya beberapa aspek sperti SDM, Teknologi, Kebijakan dll, sehingga penerapan strategi untuk industri pertahanan belum mampu mecapai kata ideal. Tujuan penulisan ini untuk mengilustratsikan strategi terbaik sehingga peran seluruh pemangku kepentingan dapat berjalan secara optimal.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0200.007

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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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 designTheoretical or conceptual
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

Citations11
Published2021
Admission routes1
Has abstractyes

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