When Is a “War” a “Wave?” Two Approaches for the Detection of Waves in Gang Homicides
Bibliographic record
Abstract
Gang violence and gang “wars” are often described as coming in waves, but little empirical work has been conducted to distinguish between actual “waves” of violence and the more common ups and downs that trends in homicides typically go through. We propose two approaches for the detection of waves of gang-related homicides in the Lower Mainland of British Columbia for a time period (2006–12) when these were considered to occur at a high rate: (1) the monthly waves approach, whereby monthly crime data are used to map the trends in gang-related violence, and (2) the micro-approach, whereby crime waves are detected by examining significant deviations from the mean number of days between homicides. The results show that four distinct monthly waves could be detected between 2006 and 2012, each capturing the peak moments of known gang conflicts. The micro-approach led to the discovery of 12 waves, allowing for a more sophisticated understanding of the trends in gang violence. While the identification of “trigger events” before the rise of a wave was relatively straightforward for the four monthly waves, not all 12 micro-waves could be associated with a clear trigger event using open source data. The two approaches should be used in complementarity for a meaningful and accurate understanding of trends in gang violence.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".