Re-Mediating the Spaces of Reality Television: <i>America's Most Wanted</i> and the Case of Vancouver's Missing Women
Bibliographic record
Abstract
In this paper I speak to debates amongst geographers and media scholars about the communities that television constructs in both ‘real’ and ‘virtual’ spaces. My point of departure is Morley's work on global television audiences, which, in focusing on the fears of white suburban viewers and the social exclusions they enact, neglects the images and narratives of television itself. The focus is a new North American television genre that addresses fears of urban crime, the ‘reality cop show’. In examining the genre's codes and practices and the ‘reality’ it creates, I try to specify the representational work which this kind of television performs. Analysis turns to a 1999 episode of Fox TV's America's Most Wanted that featured the mysterious disappearances of thirty-one women, all of whom were/are identified with the sex trade in Vancouver, Canada. As a morality tale of sex crimes and sexual dangers in the city, targeted at white North American suburban viewers, central details of the case were missing, including the fact that most of the disappeared were/are native women. The imposition of a Jack-the-Ripper “media template” displaced local and highly politicized explanations related to prostitution laws, community policing practices, and dangerous urban spaces.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.049 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".