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Diplomasi Maritim Indonesia dalam Perumusan ASEAN Outlook on the Indo-Pacific

2022· article· id· W4286680894 on OpenAlexaff
Luna Khoirunissa, Maria Indira Aryani

Bibliographic record

VenueFrequency of International Relations (FETRIAN) · 2022
Typearticle
Languageid
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

ASEAN Outlook on the Indo-Pacific (AOIP) merupakan konsep kerja sama di kawasan Indo-Pasifik yang dicanangkan oleh ASEAN yang diresmikan pada Juni 2019, di tengah maraknya berbagai pandangan tentang kerja sama di Indo-Pasifik. Indonesia yang merupakan salah satu pendiri ASEAN seringkali dikenal sebagai pelopor maupun inisiator berbagai kerja sama di kawasan. Indonesia juga merupakan penggagas utama adanya konsep AOIP. Oleh karena itu, tulisan ini bertujuan untuk menjelaskan tentang bagaimana upaya diplomasi maritim Indonesia dalam perumusan AOIP, baik melalui forum internal ASEAN serta forum mekanisme ASEAN. Upaya yang dilakukan oleh Indonesia akan dianalisis menggunakan konsep diplomasi maritime yang meliputi tiga aktivitas pokok, yakni diplomasi maritim kooperatif, diplomasi maritim persuasif dan diplomasi maritime koersif. Tulisan ini juga dianalisis menggunakan metode kualitatif dengan pendekatan deskriptif, dengan data primer yang berasal dari wawancara dengan Diplomat Kementerian Luar Negeri dan data sekunder berasal dari berbagai literatur dan berita yang berkaitan dengan topik. Artikel ini berkesimpulan bahwa diplomasi maritim Indonesia dalam perumusan AOIP dilakukan dengan di bawah kerangka diplomasi maritim koorperatif dengan menekankan pentingnya sebuah pandangan untuk menguatkan kerja sama di kawasan Indo-Pasifik di berbagai forum di ASEAN. Tulisan ini dibatasi dari tahun 2014, saat Presiden Jokowi pertama mengemukakan kebijakan luar negeri poros maritim, hingga tahun 2019 saat diresmikannya AOIP.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0580.002

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 head0.013
GPT teacher head0.219
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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