Community perceptions of Indo-Canadian gang violence in the Lower Mainland
Bibliographic record
Abstract
Over the past two decades, there have been approximately 100 murders attributed to Indo-Canadian gangs in the Lower Mainland of British Columbia: over 50% of these murders have occurred in public settings. The issue of Indo-Canadian gangs has received tremendous media attention. Community concerns have focused on the nature and extent of the recent gang violence and what should be done to respond and prevent it. Some observers have cautioned against overreacting to the involvement of Indo-Canadians, while others view it as a serious problem. Still others have debated whether the community should get involved or whether it is exclusively a police matter.\n\nAlthough limited studies have been conducted on Indo-Canadian gangs, even fewer have focused on what the Indo-Canadian community thinks about the issue. this study examined the perceptions of Indo-Canadians, how they viewed the issue, and what they felt should be done about it. In the present study, survey data from 157 Indo-Canadians residing in the Lower Mainland were examined. These finding cannot be generalized to the Indo-Canadian community at large due to the low response rate (13.6 per cent). Still, it was found that the overwhelming majority of respondents (87.2 per cent) felt gang violence was a problem in general and nearly two-thirds (61.7 per cent) felt that Indo-Canadian's were mostly responsible for the gang violence. Furthermore, family was viewed as most important in preventing gang violence, and a large majority of respondents (87.6 per cent) were willing to help address the issue of gang violence in their community.\n\nBased on the findings, it can be concluded that there is a division between perception and reality. Although Indo-Canadians engage in gang violence, the problem is often sensationalized in the media resulting in a variety of misconceptions. Indo-Canadian gang violence is not as entrenched or sophisticated compared to other crime groups in the Greater Vancouver Area. Therefore, gang violence is not just an Indo-Canadian issue, but rather a global issue affecting members of all ethnic groups. Ending gang violence requires a collaborated effort from various agencies and individuals. No one agency will be able to end gang violence on its own.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".