Introduction: Domestication of War
Bibliographic record
Abstract
This Special Section broadens and qualifies the terms through which the relationship between home and militarization has been understood. We do this by joining a vibrant and growing field of transdisciplinary scholars who address the militarization of everyday life by attending to domesticity and practices of domestication. We grapple with how the home naturalizes and becomes a catalyst for militarism: How do ordinary and domestic objects, technologies, spaces, and infrastructures make violence feel at home in the world? We are concerned with the domestic life of militarization as oikos: the household, habitat, and milieu of violent material relationships that are both ongoing and latent. The domestic is not just a discrete, private space; it also extends into public spaces like neighborhoods, local businesses, waste disposal infrastructures, hospices, and crop fields. Developed within an editorial process rooted in a feminist ethos, the articles collected here provide critical and alternative methodologies and disciplinary forms for considering militarism's aesthetics, affects, and modes of appearance. This collection resists conventional spatialities, temporalities, and incarnations of war while calling attention to the obscuring of violence through practices of care and marketing operations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".