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
Criminal and terrorist organizations often depend on repeat offenders to maintain the group’s longevity, especially after repeated law enforcement interventions. Yet, little is known about the offenders who perpetrate multiple incidents on behalf of a group. Relying on data for 118 terrorist offenders involved across eight attacks from 2000 to 2005, this study examines the correlates of repeat offending within a terrorist organization. Our main predictor, criminal social capital, is measured by the number and structure of co-offending ties. Poisson regression results demonstrate that offenders with a higher number of connections are more likely to be involved in multiple attacks; while offenders positioned as brokers—bridging otherwise unconnected others—are less likely to reoffend. In addition, being a leader and graduate education was associated with repeat offending. These findings suggest that selection is based on more than an offender’s skill set but also on their embeddedness within the group.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it