The 2011 Vancouver riot and the role of Facebook in crowd-sourced policing
Bibliographic record
Abstract
This paper considers two interlinked developments of the 2011 Vancouver riots in British Columbia, Canada: the self-righteousness of citizens on social media, and the impact this has on official police work. The use of social media is of course not new with the 2011 Vancouver riots and while other parallels exist (e.g. 2010 G20 Summit held in Toronto), a key difference rests with citizen behaviour. With this in mind we consider the use of surveillance and social media to identify and prosecute people who participated in the 2011 Vancouver riots. While Canada has seen other “Stanley Cup Riots” (e.g. Montreal 1993, Vancouver 1994), social media were not a part of these past events. We draw from three data sources: 1) Facebook postings following the 2011 Vancouver riots; 2) documents; and 3) qualitative interviews with university students and university employees conducted before the 2011 Vancouver riots. The triangulation of these data from different time periods contextualizes attitudes about surveillance strategies on social media, a process that helps to provide a more complete perspective of the use of social media following the 2011 Vancouver riots. From these data emerge a developing form of governance amongst social media users; we refer to this as crowd-sourced policing. Insights about this phenomenon can be gained by investigating the 2011 Vancouver riots. To do so, we first outline our conceptual framework, discuss police use of social media, provide an overview of our methods, develop crowd-sourced policing on Facebook, and then link this with social control on Facebook, before finally drawing our conclusions. Suggestions for future research are noted.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".