A hybrid of Borda-TOPSIS for risk analysis of Islamic state network development in southeast Asia
Bibliographic record
Abstract
In a decision-making environment related to risk, there are four basic circumstances, namely certainty, risk, uncertainty and conflict. The dynamics of the strategic environment in Southeast Asia cannot be separated from the movement of the development of the Islamic State (IS). The terror threat in Southeast Asia is currently divided into different generations of terror, namely the threat of the Al-Qaeda terror network and the threat of the ISIS terror network. This study aims to analyze and identify the risk value of the development of the Islamic State network in Southeast Asia using the Borda and TOPSIS methods. The Borda method is used to give weight to the criteria related to risk analysis. The TOPSIS method is used to provide a criteria-based risk score. This research is limited to the Southeast Asia region with 4 (four) major countries, namely Indonesia, Malaysia, Thailand, and the Philippines. This research is expected to contribute to control the development of Islamic state networks in the Southeast Asian region. Based on the results of the overall risk analysis, it was found that the Philippines has the highest risk factor value for Islamic State (IS) with a value of 0.550 at level 4 in the High category. Indonesia maintains a risk factor value of 0.307. Thailand has a risk factor value of 0.427. Indonesia and Thailand are at level 3 with the Medium category. Meanwhile, Malaysia has a risk factor value of 0.203 at level 2 in the Low category.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".