The Process of Proving Participation in the Crime of Terrorism in Indonesia
Bibliographic record
Abstract
Objective: Terror is different from war; acts of terrorism are not subject to the procedures of warfare because the implementation is always sudden and the target victims are often civilians who are in crowded places. In early 2018, the tragedy of terrorism inmates at the Mobile Brigade Command Detention Center (Mako Brimob) in Kelapa Dua, Santa Maria Tak Bercela Church, GKI Diponegoro, Surabaya Central Pentecostal Church Sawahan Congregation, and Makassar Cathedral Church in March 2021 shook hearts with extraordinary horror. In an instant, there were victims of death, trauma, injuries, and lifelong disabilities. So that this can be categorized as a crime against humanity and a serious threat to the sovereignty of the Unitary State of the Republic of Indonesia. Purposes of the Research: This research aims to examine and discuss the process of proving participation in the crime of terrorism in Indonesia. Methods: This type of research is normative juridical research, conducted by examining library materials or secondary data using a statutory and conceptual approach. Results and Discussion/Value: Judges play a crucial role in establishing justice during the proof process in the trial of terrorist crimes since the criminal act of terrorism has expanded and taken on multiple interpretations in the law. In order to convict a suspect as a result of an actual act and not just a notion or an ideology that cannot be used as an error, participation must be properly evaluated before labeling the suspect as the culprit in this case.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".