Public Criminology and the 2011 Vancouver Riot: Public Perceptions of Crime and Justice in the 21st Century
Bibliographic record
Abstract
Facilitating public debates about crime and its various facets is at the core of public criminology. Public criminology focuses on the role that criminologists play in the manner that they contribute to public debates related to crime. I suggest herein that empirically investigating public opinions offered in response to criminal events like riots on social media sites can contribute to a more informed sense of various public understandings about crime, a process that serves as a point of entree for the public criminologist. I provide a brief summary of the 2011 Vancouver riot in British Columbia and outline how social media brought increased attention to the riot that in turn helped people to make sense of the riot. I briefly explain how to deal methodologically with select materials gathered from social media and then develop some basic user-generated themes relating to the riot, including, user production of evidence and punishment. I conclude with a short discussion for how this might contribute to a public criminology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".