Hit Them Where it Hurts: State Responses to Biker Gangs\nin Canada
Bibliographic record
Abstract
From civil and criminal forfeiture, to "gangsterism"offences in the Criminal Code, Canada does not lack for tools to address biker gangs. Yet attempts to stamp out bikers have met with little to no success. State responses to criminal organizations should use those organizations' own structures and symbols of power against them. A gang's reputation may be effectively used against a gang, but this strategy poses significant challenges to prosecution. Attempts to use a gang's internal hierarchy and administrative structure can succeed, but may only produce circumstantial findings if not supported by sufficient and substantial evidence. Attempts to combat gang violence by targeting their clubhouses, whether through forfeiture provisions or through municipal bylaws, may prove the most effective methods of targeting biker gangs. The issue is not a lack of resources; those resources are used inefficiently and ineffectively
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; both teacher heads agree on what is shown here.
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".