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Record W3007134738 · doi:10.32834/jplan.v1i2.139

IMPLEMENTASI KEBIJAKAN PEMBINAAN POTENSI MARITIM TERHADAP PEMBERDAYAAN WILAYAH PERTAHANAN LAUT

2020· article· en· W3007134738 on OpenAlexaboutno aff
Yogi Suwarno, Bernadius Yules Verne, Iwan Pribadi

Bibliographic record

VenueJournal of Public Policy and Applied Administration · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal Management and Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Indonesia is geographically an island nation with two-thirds of the ocean area larger than the mainland. This can be seen with the coastline on almost every island in Indonesia (± 81,000 km) which makes Indonesia ranks second after Canada as the country that has the longest coastline in the world. This power is a great potential for advancing the Indonesian economy. The extent of the ocean compared to the total land area in the world reaches approximately 70 to 30, making it a challenge for countries in the world that have the interests of the sea to advance their maritime. As the development of the strategic environment, the role of the sea becomes significant and dominant in delivering the progress of a country.Keywords: Island States, Strategic Environment 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.033
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0330.003

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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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