Indonesian National Security Policy in Fighting Terrorism Among the Youth Generation
Bibliographic record
Abstract
At the beginning of the 21st century, Indonesia was marked by terrorist attacks that caused victims, such as the Bali Bombings in 2002. The threat and violence of terrorism cannot be separated from the influence of international terrorist organizations that attack Indonesia through targeted attacks on vulnerable individuals or groups, more specifically the youth generation. The perpetrators of terrorism have taken advantage of the technological network of the online radicalization era. The Industrial Revolution 4.0 has inspired many patterns of human interaction from domestic interactions to global interactions. This study explains various ideas about implementing national security policies in countering terrorism among the youth generation with qualitative methods using literature analysis so that several tactical steps are found to counter terrorism through critical and open education, exemplary, eradicating injustice, transcendence, and international cooperation. An important finding in this study is the importance of the joint commitment of elements of society to implement Indonesia's national security policy through actions that have small dimensions in the school and household environment and large dimensions at the national level.
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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 | 0.002 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".