Bibliographic record
Abstract
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.042 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".