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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0120.003

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