Discriminating Tastes: ‘Smart’ Bombs, Non-Combatants, and Notions of Legitimacy in Warfare
Bibliographic record
Abstract
Abstract Much has been made in recent years of the remarkable technological advances driving what has been described as the latest Revolution in Military Affairs (RMA). Typically, however, a disproportionate emphasis on the astounding capabilities of new military hardware has come at the expense of investigations into the socio-political consequences of the transformation of warfare presently underway. Reflection upon the less neglected social aspects of previous RMAs is instructive, suggesting that technological determinism does not yield reliable accounts of the most important implications of new military technologies. In light of this, a historically informed reconsideration of prevailing assessments of the nature and significance of the current RMA seems in order. In particular, rapidly evolving attitudes toward discrimination between combatants and non-combatants in warfare are in need of consideration, as these have traditionally been bound up with watershed military innovation. Implicated in the reversal of a trend toward greater tolerance of indiscriminacy, the advent of precision-guided munitions (PGMs) increasingly bears directly on perceptions of legitimacy in the conduct of war. In this context, unequal access to PGMs suggests unequal legitimate recourse to war measures, and this might well turn out to be the most important implication of the RMA to which we are witness.
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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.094 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".