JARINGAN TERORIS SOLO DAN IMPLIKASINYA TERHADAP KEAMANAN WILAYAH SERTA STRATEGI PENANGGULANGANNYA (Studi Di Wilayah Solo, Jawa Tengah)
Bibliographic record
Abstract
The Solo terrorism network was affected by the Darul Islam which had mutated into radical Islam groups and then into new terrorist group. The main reason was new political motive with religious cover and supported with other factors. The purpose of this research was to discovered the origin, extend, and cause of the terrorism establishment and to discover the Indonesia’s terrorism. The object of this research was Solo terrorism network as the biggest terrorist network in Indonesia with connection to various countries, and also all Indonesia terrorism related to this network. This research was conducted with in depth interview on the terrorist victims, agency, and former sect member who considered as terrorist. It showed that the Solo new group network was more amateur than the older one even though the ideology was similar. The Solo terrorism actions had affected the regional security especially the ideology, politics, economic, social and human security. Terrorism prevention in Solo had caught and dismantle Solo new terrorism network although with various flaws that needed to be fixed. The effective terrorism prevention strategies were law enforcement, prevention, deradicalization, and disengagement which had to well organized and shared together. Keywords: Solo Terrorist Network, Regional Security, Prevention Strategy
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".