“The darkest time in our history”: An analysis of news media constructions of liquor theft in Canada’s settler colonial context
Bibliographic record
Abstract
In September 2018, there was a surge of news stories about liquor store theft in Winnipeg, Canada that resulted in public and political calls for action, and ultimately led to the introduction of a range of new security and surveillance measures at government owned liquor stores. This brief news cycle provided opportunities for various social actors, politicians, and authorities to make claims about the nature of crime and society more broadly. This article analyzes recent news media coverage of liquor store theft in Winnipeg, Canada and the social construction of an ostensibly new crime trend in the city: “brazen” liquor store thefts. We employ a qualitative content analysis of news articles about liquor store theft published in local Winnipeg news media between 2018 and 2020 (n = 147). Drawing on the social constructionist paradigm, and Fishman’s conceptualization of “crime waves,” we argue that the framing of liquor theft via news media reflects longstanding cultural tropes and myths about crime, as well as hinting at but never fully confronting, deeply engrained colonial and racialized stereotypes. This paper contributes to our understanding of the ways putative social problems are made intelligible in the media. We demonstrate how “crime waves” are shaped by and shape dominant tropes about crime, safety, and citizenship. We argue that something as mundane as liquor theft reveals much about the historical, colonial and social roots of crime in local and national contexts.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.025 | 0.019 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".