Bibliographic record
Abstract
This thesis examines whether it should be possible for civilians to hold states liable for losses states inflict on them during warfare. Answering this question requires turning into two bodies of laws that appear to be incompatible and unable to yield clear conclusions. First, the laws of war, which regulate states’ conduct in combat, but do not provide individuals with a private law claim-right against states or impose duties of compensation on states towards civilians. Second, tort law, which offers civilians a cause of action, yet its structure seems difficult to apply in the battlefield, and its availability is frustrated in many common law jurisdictions by a special immunity. Consequently, arguments relating to the liability of states tend to be polarized, advocating for either complete immunity or total liability, and are divorced, to various degrees, either from the laws of war or from tort law. In this thesis, I develop a novel account of the tortious liability of states for wrongs they inflict during combat that is informed by the laws of war, tort law theory and doctrine, and substantive rule of law principles. My central claim is that by examining the laws of war, it is possible to articulate the rights and duties of states and civilians during war, including what amounts to an imposition of wrongful losses for which corrective justice duties arise. Only losses that are inflicted while violating the laws of war are wrongs for which liability can and should be imposed, as such actions are outside the scope of states’ authority. By exposing the nexus between the laws of war and tort law and defining which losses amount to belligerent wrongs, the framework I offer illuminates how tort doctrines can apply in the battlefield and why tort liability should be available for civilians against states.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".