Making Crime TV: Producing Fictional Representations of Crime for Canadian Television
Bibliographic record
Abstract
Criminologists and sociolegal scholars have become increasingly interested in studying media representations of crime in popular culture. They have studied representations using content analyses, often examining their “accuracy” against academic research. Alternatively, these scholars have also studied media effects. In contrast to these studies, I focus on the television production process of making entertaining, dramatic representations of crime. In doing so, I empirically address the following research question: how do TV writers know about crime, and how do they transform that knowledge into fictional representations? I answer this question using a triangulation of methods to gather data – specifically, ethnography, archival research, and interviews with writers and producers – and through the juxtaposition of several case studies. My case studies include the following Canadian crime television programs: 1) the police drama 'The Bridge,' 2) an original Canadian drama about insurance fraud, 'Cra$h and Burn,' and 3) crime docudramas, such as 'F2: Forensic Factor' and 'Exhibit A: Secrets of Forensic Science.' Taking cues from Bruno Latour’s actor-network theory, I focus on the site-specific, concrete, dynamic processes through which each television production makes fiction. I conceive of the writers’ room as a laboratory that creates representations through collaborative action and trial and error. This research demonstrates that, during the production process, representations of crime are unstable, constantly in flux as various creative and legal entities compel their revision. Legal entities, such as Errors and Omissions insurance and broadcasters’ Standards and Practices, regulate the content and form of representations of crime prior to their airing. My findings also reveal the contingency of (commercial) success, the heterogeneity of people who make up television production staff, and the piecemeal state of knowledge that circulates between producers, network executives and writers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".