Bibliographic record
Abstract
Of the 13,000 works of art in the Canadian War Museum’s holdings, only 64 display dead bodies. Prevailing explanations of this absence revolve around respect for the dead and ethical responsibility to avoid the glorification of war. And yet death and destruction are pervasive in war. The irony is that one leaves the museum with the sense that war does not produce corpses, or at least not very many of them. Nowhere is this irony more evident than in the Canadian War Museum’s armaments collection, described as ‘the way in which human ingenuity has been applied to the science of war, creating weapons and other devices to attack, protect and kill’, but with only technical information about weapon calibre and capacities provided. This article describes an effort to dig up the dead. Studying the form and function of the labels accompanying weapons, I argue that seemingly mundane technical specifications classify and standardize certain kinds of bodily injury and death, and make the bodies destroyed by war present. Overall, arguing that injury and death are in the (technical) details, I challenge the assumption that a focus on technological devices sanitizes war. Instead, I propose a way to investigate and interrogate how death and injury in war are calibrated and embodied in the standards that make weapons ‘conventional’.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.037 | 0.015 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".