Bibliographic record
Abstract
Amidst a debate during the 1970s about the wounding capacities of new smaller calibre, high-velocity ammunition, concerns were raised about whether the unprecedented wounds produced by this new kind of weaponry were consistent with principles and provisions to prevent unnecessary suffering and superfluous injury outlined in international humanitarian law. Essentially a question of whether these wounds were ‘humane’, in this paper I explain why the novel forms of injury under review were not limited but legitimated within the conduct of war. Detailing a process in which the morphology of wounds – such as the type and extent of tissue damage – becomes linked to the very morality of waging war, I argue that the category of a ‘normal’ wound emerged as a referent that not only settled disputes over how to interpret the law of war but in the process made certain kinds of violence allowable. Wounding, I argue, mediated the laws of war, and laid the basis for a general legal framework that continues to set the ethical and legal parameters of war. More broadly, I interrogate the evolution of the concept of normal wounds to show that the ongoing question of making war humane requires attention not only to how war’s violence has been restrained but equally to how and why certain forms of physical dismemberment and debilitation have been rendered acceptable and lawful. It calls for exploring and excavating war wounds as an archive not just of the horrors of war but also the apparent humanity of large-scale armed violence.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.007 |
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".