Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acts of terrorism can be harrowing and cause extensive damage, yet they occur far too frequently. How do terrorist groups organize and coordinate their attacks? What makes those groups simultaneously cohesive and flexible in a hostile environment? Different academic disciplines have contributed to a better understanding of the proliferation of terrorist acts in recent years. With very few exceptions, however, extant psychological research on terrorism has almost exclusively focused on the individual terrorist. We leverage the team literature to better understand how a team of terrorists radicalizes, organizes, and makes decisions. Drawing from the work of Weick (1976), we characterize terrorist teams as loosely coupled systems. Examples of different terrorist attacks from the last 15 years illustrate how loose coupling in terrorist teams is especially powerful because of the high familiarity and intimacy among members of terrorist teams. Loosely coupled structures have led to highly adaptive and resilient teams whose actions are fluid, unpredictable, and often lethal. We conclude by discussing implications for counterterrorism and for future research. (PsycINFO Database Record
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.006 |
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 it