The Search for Suitable Homicide Co-Offenders Among Gang Members
Bibliographic record
Abstract
Little is known about homicide co-offending networks at the individual gang member level. Of particular interest is whether and to what degree gang members who are selected to participate in murder are different from those who are not. The current study constructed the co-offense network of 18 participants from the Incarcerated Serious and Violent Young Offender Study who were identified as members of a prominent gang within British Columbia, Canada, referred to as the BC Gang. This gang started to form not long before seven offenders together committed a homicide that was orchestrated by the founder and leader of the BC Gang. After this offense, some of these seven offenders became the most central actors within a large network of co-offenders ( n = 137) that was measured at four time periods over a 20-year period. Over this period, a second murder, like the first, was orchestrated by the leader of the BC Gang, offering a rare glimpse into the co-offending recruitment decisions made by a high ranking gang member for two separate homicides. Although only 25% of the 137 co-offenders are BC gang members ( n = 35), 100% of the offenders selected for a homicide were members of this gang ( n = 13). The network contained 8 separate components at the final measurement period, but all 13 homicide offenders were connected to the same network component of 48 individuals.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".