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Record W1936201598 · doi:10.3138/topia.23-24.286

Mil-bot Fetishism: The Pataphysics of Military Robots

2010· article· en· W1936201598 on OpenAlexvenueno aff
Ian Roderick

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicAnthropology: Ethics, History, Culture
Canadian institutionsnot available
Fundersnot available
KeywordsFetishismRobotArtificial intelligenceSociologyContext (archaeology)MilitarizationRevolution in Military AffairsLawPolitical scienceComputer securityComputer sciencePoliticsMilitary scienceHistory

Abstract

fetched live from OpenAlex

This paper begins by identifying a tendency in the mass media to represent military robotics in a manner that endows the devices with a degree of automation and agency that is actually beyond the technology. Military robot fetishism is not simply based upon an irrational or mistaken belief about the real capacities of the robots but, instead, their fetish value stems from their positive valuation according to a code of functionality (Baudrillard) that rests upon the risk-transfer labour of the robot. Acting as (Western) soldier surrogates, the promise of the military robot is one of casualty reduction but asymmetrically so. This fetishism arises, as Mulvey proposes, out of the difficulty of representing military reality— namely that waging war has not become a scientifically guided rational-antiseptic enterprise but continues to be a gruesome and violent activity. The fetishization of military robots can be attributed to the need to ameliorate a reality that is politically difficult for western governments and their militaries. In this context, military robotics becomes a science of imaginary technical solutions to the problem of war legitimation. The promotion of military robot fetishism in the mass media means that the military robot as fetish comes to circulate within both martial and civilian lifeworlds, re-legitimizing warfare and affording further militarization of civic life.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.005
Scholarly communication0.0000.000
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 head0.056
GPT teacher head0.350
Teacher spread0.294 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations16
Published2010
Admission routes1
Has abstractyes

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