Bibliographic record
Abstract
Kasus residivis terorisme menunjukkan adanya celah dalam pelaksanaan program deradikalisasi di Indonesia, salah satunya pada tahap assessment. Tahap tersebut perlu melibatkan berbagai pihak, karena assessment yang dilakukan terhadap kelompok teroris harus secara mendalam. Penelitian ini dilaksanakan untuk memberikan gambaran tentang peranan intelijen dalam assessment dan evaluasi pada program deradikalisasi. Penelitian menggunakan metode kualitatif yang bersifat deskriptif. Peneliti mengklasifikasikan narapidana dan mantan narapidana terorisme berdasarkan respon terhadap program deradikalisasi untuk menentukan prioritas bagi intelijen dalam mendukung assessment. Data yang digunakan adalah data primer berupa catatan hasil observasi dan wawancara mendalam terhadap tujuh mantan narapidana terorisme. Selain itu, peneliti menggunakan data sekunder berupa tinjauan literatur hasil penelitian terdahulu mengenai deradikalisasi dan residivisme terorisme, serta pemberitaan media online.Berdasarkan temuan penelitian, penting untuk melakukan assessment yang komprehensif dan berlapis terhadap individu yang pernah menjadi terpidana terorisme. Penilaian ini harus didukung oleh informasi intelijen untuk memastikan akurasi yang lebih baik. Evaluasi yang tepat akan memungkinkan penerapan program deradikalisasi secara efektif dan membantu mengurangi kemungkinan terjadinya residivis terorisme.
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 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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.007 |
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".