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Record W3167628072 · doi:10.6000/1929-4409.2021.10.102

Formulation of Correctional System Model in Corruption Enforcement in Indonesia

2021· article· en· W3167628072 on OpenAlexvenueno aff
Dinar Mahardika, Tian Terima, Kamal Fahmi Kurnia, Aditya Erwin Pratama, Arif Zainudin

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Policy Analysis in Indonesia
Canadian institutionsnot available
Fundersnot available
KeywordsPunishment (psychology)DistrustLanguage changeImprisonmentSanctionsNormativePrisonPoliticsLaw enforcementCompromiseEnforcementLawLaw and economicsCriminologyPolitical scienceEconomicsSociologyPsychologySocial psychology

Abstract

fetched live from OpenAlex

Corruption in Indonesia requires the right formulation. The rule of law and punishment system in Indonesia is still weak against criminals. This paper tries to find the right formula in punishing corruptors because the existing punishment system is still breakable and is deemed ineffective in preventing it. This paper belongs to a type of normative approach with a descriptive-analytical. This paper reveals that there are weaknesses in the rule of law in corruption compromise, resulting in increasing corruption every year. Data from ICW in 2018 shows that corruption is still considered a common crime, and the current punishment system does not reflect the firmness in determining the maximum punishment. It is exacerbated by the sale and purchase of facilities in prisons, which results in public distrust of the law. In other countries, Indonesia must be more severe in punishing bribery and corruption, including extraordinary crimes against the people. The conclusion in this paper is to formulate a religious and moral approach in punishing corruptors such as social sanctions, public humiliation, being a cleaning worker, life imprisonment, being exiled, taking political rights, even being humiliated in public so that it becomes a new formula in corrupt penalty system.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.207

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 studies0.0000.000
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 head0.067
GPT teacher head0.366
Teacher spread0.298 · 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 designTheoretical or conceptual
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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Criminology and SociologySame topicLegal and Policy Analysis in IndonesiaFrench-language works237,207