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Record W4401062072 · doi:10.33087/legalitas.v16i1.556

Urgensi Penyatuan Lembaga Penegak Hukum di Laut Indonesia

2024· article· en· W4401062072 on OpenAlexaff
Andi Muhammad Ridha, M Muhammad Soffan Heri, Bayu Setiaji

Bibliographic record

VenueLegalitas Jurnal Hukum · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsWiLAN (Canada)
FundersKementerian Keuangan Republik Indonesia
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

In its efforts to enforce law at sea, Indonesia has 13 law enforcement agencies at sea. Six of these institutions have maritime patrol task forces. The number of law enforcement agencies in Indonesian seas is considered negative by several parties because of the potential for the authority of each agency to overlap. This research aims to find out what the condition of maritime law enforcement agencies in Indonesia is and what are the considerations for unifying maritime law enforcement agencies in Indonesia. This research is normative research related to maritime law enforcement agencies in Indonesia. There are at least 13 law enforcement agencies in Indonesian seas. The unification of maritime law enforcement agencies in Indonesia is necessary because unclear roles and authority, as well as a lack of coordination between agencies, have resulted in ineffectiveness in law enforcement and maritime security management. Indonesia needs unification of authority to strengthen all security, safety and maritime law enforcement functions in Indonesia

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), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.878
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 studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.336
Teacher spread0.306 · 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 teacher head, not a consensus.

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
Published2024
Admission routes1
Has abstractyes

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