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Record W4404444905 · doi:10.33756/jelta.v17i2.26079

The Principle of Audi and Alteram Partem in The Process of Proof in Criminal Cases (Analysis of Decision Study No. 123/PID.B/2022/PN YYK)

2024· article· en· W4404444905 on OpenAlexaff
Hartanto Hartanto, Susanto Susanto, Daniil Romanovich Alimpeev

Bibliographic record

VenueJURNAL LEGALITAS · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Policies
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsPID controllerControl theory (sociology)Process (computing)Computer scienceControl engineeringLawEngineeringPolitical scienceControl (management)Artificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

A decision is a legal product issued by a judge in resolving a case presented at trial. A decision must reflect a sense of justice obtained from the facts revealed at trial and compliance with existing laws and regulations. Legal considerations in a decision aim to explore the facts revealed in the trial based on the principle of audi et alteram partem which must exist and be the basis. The application of audi et alteram partem aims to ensure that the panel of judges carefully listens to the arguments and facts put forward by both parties, namely the public prosecutor (JPU) or legal advisor. Therefore, the aim of this research aims to describe how the panel of judges examined case Number 123/Pid.B/2022/PN Yyk by applying the principles of evidence in the Criminal Procedure Law. To answer these legal problems, this research uses a combined research method of normative and empirical data with data collection methods by conducting interviews and literature reviews. The research results show that the panel of judges who examined case Number 123/Pid.B/2022/PN Yyk were assumed to have ignored the principle of audi et alteram partem in their decision. This principle of balance (justice) has not been fully fulfilled, because the evidence in the trial has not reached conformity, thus making the defendant position weaker.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.363
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.405
Teacher spread0.369 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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