Investigating the Relationship Between Drone Warfare and Civilian Casualties in Gaza
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs), better known as drones, are increasingly touted as ‘humanitarian’ weapons that contribute positively to fighting just wars and saving innocent lives. At the same time, civilian casualties have become the most visible and criticized aspect of drone warfare. It is argued here that drones contribute to civilian casualties not in spite of, but because of, their unique attributes. They greatly extend war across time and space, pulling more potential threats and targets into play over long periods, and because they are low-risk and highly accurate, they are more likely to be used. The assumption that drones save lives obscures a new turn in strategic thinking that sees states such as Israel and the US rely on large numbers of small, highly discriminating attacks applied over time to achieve their objectives. This examination of Israel’s 2014 war in Gaza argues that civilian casualties are not an unexpected or unintended consequence of drone warfare, but an entirely predictable outcome.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it