Bibliographic record
Abstract
During the first decade of this century, terrorist attacks claimed thousands of lives in New York, London, Madrid, Bali, Jakarta, Mumbay, Istanbul, Ankara, Amman, Riyadh, Baghdad, Kabul and many other cities in Iraq, Afghanistan and around the world. Meanwhile, in many countries such as Canada, England, Germany, Pakistan and the U.S., among others, significant terror plots have been disrupted. Clearly the international community is facing a terrorist threat of historical proportions; countering this threat requires an understanding of both the intensions and capabilities of terrorist groups and individuals (lone wolves) to carry out violent acts. The national counterterrorist strategies mainly focus on constricting the capabilities of terrorists (through military action and disrupting the financial and logistics networks, all of which require a significant amount of intelligence capabilities) and destroying their will to attack. This is particularly the case when addressing the threat of terrorists who seek to acquire and use weapons of mass destruction (WMD). However, there is much more that nations and international organizations can do to understand and counter the ideological motivations behind the threat of catastrophic terrorism.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".