Bibliographic record
Abstract
Consequence Conditions Just war theory, the traditional theory of the morality of war, is not a consequentialist theory, since it does not say a war or act in war is permissible whenever it has the best consequences. On the contrary, its jus ad bellum component, which concerns the morality of resorting to war, says a war with the best overall outcome can be wrong if it lacks a just cause, that is, will not produce a good of one of the few types, such as resisting aggression or preventing genocide, that alone can justify war. It can likewise forbid a war that is not declared by a competent authority or fought with a right intention. Similarly, the theory's jus in bello component, which concerns the morality of waging war, contains a discrimination condition that can forbid military tactics with the best outcome if they target civilians rather than only soldiers. In all these ways the theory is deontological rather than consequentialist. But just war theory does not ignore the consequences of war and would not be credible if it did: a morally crucial fact about war is that it causes death and destruction. The theory therefore contains several conditions that forbid choices concerning war if their consequences are in some way unacceptable. The jus ad bellum insists that a war must have a reasonable hope of success in achieving its just cause and other relevant benefits; if it does not, its destructiveness is to no purpose and the war is wrong.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".