Toronto’s Little Shop Of Horrors: A Cultural Criminology Examination On Serial Killer Bruce McArthur And The News Media
Bibliographic record
Abstract
<div>Contributing to the dynamic and interdisciplinary field of cultural criminology, this project works to emphasize the destructive, modern forces of consumerism and violence within Toronto’s crime-news industry. The paper fuses the canonical and emerging methodologies of content analysis, discourse analysis, and liquid ethnography, to evaluate the framing and editing techniques used to relay the story of Bruce McArthur’s predations in The Village (over the 2018 news year). A sample of 365 articles, retrieved from five print media sources, are methodically examined to understand both the local and national agenda-setting strategies of contemporary journalism. Actively contributing to the transformation of human suffering and violence into mass-market pleasure, a carnival of crime model (Presdee, 2000) serves as a primary lens for evaluating the hyper-sensationalized reporting styles of modern news makers. Weaving theoretical contributions from the fields of sociology and media studies, the embeddedness of heteronormative, racialized, and ethnocentric tropes common to the news and crime-infotainment industries is also critically evaluated towards raising greater political and social accountability. Crime-centric podcasts are further identified as a leading technological medium for fueling public obsessions with murder and transgressions. Formed by enthusiastic hobbyists and motivated journalists, the producers of podcasting content hastily straddle the realms of entertainment and information sharing. As such, this research calls for immediate awareness and tending to the neoliberal symptoms of boredom and fear existing in our modern world, building on Stanley Cohen’s (1972) moral panic theory.</div><div><br></div><div>Keywords: cultural criminology, serial killer, news media, crime infotainment, McArthur<br></div>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".