“I don’t like guns but having one in Winnipeg right now would feel safer”: Crime, liquor theft, and online fantasies of punishment and control
Bibliographic record
Abstract
In 2018–2019, there was a surge of local news media coverage in Winnipeg, Canada about what news media described as “brazen” liquor store theft. Online discussion and social media platforms provided segments of the public with opportunities to assert claims about the causes and consequences of this putative crime wave as well as potential solutions within and outside the penal system. These online fora allowed internet communities to imagine new methods of crime control and vocalize a range of emotions about crime and punishment. Employing a thematic analysis of reader comments across several online and social media platforms, we argue that these online discussions about liquor theft provide an empirical case study of the new digital media logic that facilitates highly volatile and short-lived moral panics or “firestorms.” We draw upon cultural criminology scholarship to highlight the centrality of emotion in online discussions on liquor theft. By making emotion central, online discussions provide a clearer glimpse into the displaced rage that is thought to set moral panics in motion. We argue that online platforms constitute an important medium to understand how people imagine and envision punishment and control, as well as how they construct/conceive of offenders and the purported problems underlying crime.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".