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Record W1536823512 · doi:10.24908/ss.v9i1/2.4100

Being Watched Watching Watchers Watch: Determining the Digitized Future While Profitably Modulating Preemption (at the Airport)

2011· article· en· W1536823512 on OpenAlexaff
Matthew Tiessen

Bibliographic record

VenueSurveillance & Society · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTransparency (behavior)Internet privacyComputer securityLawSociologyTrespassHabeas corpusAestheticsClothingComputer scienceLaw and economicsPolitical scienceArt

Abstract

fetched live from OpenAlex

Gilles Deleuze once wrote in “Postscript on the Societies of Control” (1992) that in the future (our present) our societies would be controlled or “disciplined” using subtly unobtrusive and strategically applied forms of “modulation.” That is, the rigid physical enclosures of Foucault’s disciplinary society would inevitably yield to more flexible, immaterial, and imperceptible forms of modulation that continually respond and adapt to life’s unpredictability. In this paper I describe how the use of naked body scanners at today’s airport is a most suitable expression of this dematerialized form of discipline, seeming at the same moment to both threaten and protect privacy, to be both non-intrusive and invasive, to both prepare for and determine seemingly unknowable but inevitable futures. The flying public, meanwhile, is caught in the confusing middle, not knowing what to believe. They find themselves trapped in an undefined surveillance grid that both threatens and protects their freedoms. Will the scanners see through clothing and catch underwear-bombs, or won’t they? Will security agents scan, save, and distribute their naked images or won’t they? The public is left with questions rather than answers. This whole (visual) apparatus which was designed to create clarity and transparency seems opaque. I suggest, then, that the opacity both of the issues at stake as well as of the scanned images of our naked bodies, confounds our categories and challenges long taken for granted social conventions about, for example, habeas corpus, privacy, security, the present, the future, potentiality, etc. Appearances, it seems, are still deceiving – even if what’s being made to appear are high-resolution scans of our naked bodies.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.039
GPT teacher head0.286
Teacher spread0.246 · 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.

Study designObservational
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

Citations8
Published2011
Admission routes1
Has abstractyes

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