The war against terrorism in south east Asia: Singapore's framework of counter-terrorism
Bibliographic record
Abstract
Many analysts have pointed out that Southeast Asia is the 'Second Front' for terrorist operations after the September 11 incident in 2001. However, terrorism is not a new phenomenon in this region. The September 11, 2001 attacks by Al Qaeda had enormous and unanticipated consequences for the international system; a protracted international conflict has been re-introduced to the world since the end of the Cold War, it also signalled a new type of threat by non-state actors to governments and civilians, and called into doubts regarding the capacities and abilities of the governments, as well as the many accepted security and administrative practices; lastly, the attacks also highlighted that political, economic, social and cultural differences between various regions of the world can be translated into violence (Foreign Affairs, Canada, 2002). When Southeast Asia was named the 'Second Front' by the United States, and with the discovery Jemaah Islamiya in Singapore after the September 11 attacks, governments have noticed that regional cooperation is essential for countering transnational terror cells (Simon, 2003: 1). Interstate cooperation has centered on the Singapore-Malaysia-Indonesia nexus. September 11 has led to a new phase of counter-terrorism campaign in Southeast Asia with two main components: intra-regional cooperation and their collaboration with Western powers, chiefly the United States (Acharya, 2004: 141). Southeast Asian governments have recognised that regional cooperation is essential for countering transnational terrorism. However, interstate cooperation has faces a number of constraints due to the different perspectives and political restrictions of different countries, therefore making it difficult to work out a common response. Nevertheless, Southeast Asian governments have taken important steps to combat terrorism reducing the vulnerability of the region to terrorism (Australian Government, 2004: 61).
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".