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Record W1799975633 · doi:10.17645/mac.v3i3.286

Attaching Hollywood to a Surveillant Assemblage: Normalizing Discourses of Video Surveillance

2015· article· en· W1799975633 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMedia and Communication · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia Studies and Communication
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsHollywoodAssemblage (archaeology)Normalization (sociology)SociologyMedia studiesArtSocial scienceHistoryArt historyArchaeology

Abstract

fetched live from OpenAlex

This article examines video surveillance images in Hollywood film. It moves beyond previous accounts of video surveillance in relation to film by theoretically situating the use of these surveillance images in a broader “surveillant assemblage”. To this end, scenes from a sample of thirty-five (35) films of several genres are examined to discern dominant discourses and how they lend themselves to normalization of video surveillance. Four discourses are discovered and elaborated by providing examples from Hollywood films. While the films provide video surveillance with a positive associative association it is not without nuance and limitations. Thus, it is found that some forms of resistance to video surveillance are shown while its deterrent effect is not. It is ultimately argued that Hollywood film is becoming attached to a video surveillant assemblage discursively through these normalizing discourses as well as structurally to the extent actual video surveillance technology to produce the images is used.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.058
GPT teacher head0.350
Teacher spread0.292 · 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