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Record W3181840603 · doi:10.6000/1929-4409.2021.10.138

Barriers to the Implementation of the Articles of Continuing Acts in the Law of Criminal Acts of Corruption in Indonesia

2021· article· en· W3181840603 on OpenAlexvenueno aff
Farida Kaplele, Sigid Suseno, Lies Sulistiani, Elis Rusmiati

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIndonesian Legal and Regulatory Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLanguage changeParagraphCriminal codeLawCriminal lawState (computer science)IndonesianCriminal procedurePolitical scienceWeightingAction (physics)CriminologySociologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The problem that will be discussed in this paper is the problem of obstacles to eradicating criminal acts of corruption regulated in the Corruption Crime Act in Indonesia, which in its implementation is often associated with the existence of norms in Article 64 Paragraph (1) of the Indonesian Criminal Code which regulates criminal acts. (voorgezette handling) corruption in Indonesia. To overcome this problem, a search for documents and literature studies was carried out, a study of laws and regulations, including decisions on corruption cases that had existed, then carried out a descriptive analysis to solve the problem. The study results show that the obstacles in the application of Article 64 of the Criminal Code are related to continuing acts of corruption in Indonesia. First, the difficulty of separating a criminal act as a single offense if it is carried out by state officials who handle the same problem and project every time. Day; Second, it is often interpreted that the will's decision is an act of corruption itself; Third, the determination of material actions (feit materieele); and Fourth, continuous action is not only seen as a rule relating to the issue of the imposition of crime and the weighting of criminal acts.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.356
Teacher spread0.312 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2021
Admission routes1
Has abstractyes

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