The 40 Members of the Toronto 18: Group Boundaries and the Analysis of Illicit Networks
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
Increases in studies on the network dynamics of crime groups and co-offending has led many scholars to reflect on potential measurement biases arising from a reliance on official data sources. A problem of official data is that it forces boundaries on criminal groups that are much more fluid and dynamic than they seem. Drawing from interviews with an individual embedded in a terrorist organization and court documents records, we apply longitudinal network methods to examine the extent to which official data influences assessments of a criminal group. Findings show that only a minority of participants interacting with the group were charged for a crime. Yet the majority had an impact on the evolution of the group. Ignoring non-criminal affiliates masks the full scope of covert groups and the variation that can assist in understanding how groups emerge and evolve.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it