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Record W2064278617 · doi:10.1068/a34134

Re-Mediating the Spaces of Reality Television: <i>America's Most Wanted</i> and the Case of Vancouver's Missing Women

2002· article· en· W2064278617 on OpenAlexaffabout
Beverley A Pitman

Bibliographic record

VenueEnvironment and Planning A Economy and Space · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReality televisionWhite (mutation)NarrativeMoralitySociologyMedia studiesReality tvTelevision studiesGender studiesPolitical scienceLawArt

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.361

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.238
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations33
Published2002
Admission routes2
Has abstractyes

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