Bibliographic record
Abstract
Abstract This chapter reviews the distinction between legitimate and illegitimate killings in the jus in bello, the laws of wartime behavior. It explores how the laws of war were drafted by the powerful to favor themselves, noting that the strong disdain restraint by law because they can, while the weak disregard law in war because they must. It critiques the Lieber code said to have advanced the laws of war as a wartime justification for hard war against any resistance to state authority. That leads to extremism at both ends with civilians trapped in the middle. The chapter talks about targeting persons for torture or killing who would be classified as civilians, an established pattern in First Nations conflicts in the Americas. Precolonial wars were fought down to the village level between Iroquois and Algonquin in upper New York, Lower Canada, the Great Lakes, and St. Lawrence River valley. It all got worse in the 20th century, as major belligerents in the two world wars and afterward all turned to blockade, starvation, and terror tactics to break civilian morale.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.380 | 0.002 |
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; both teacher heads agree on what is shown here.
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".