Bibliographic record
Abstract
The contemporary literature on military ethics has a long and rich pedigree. Anchored in the work of the ancient Greeks and Chinese, it was initiated in the Middle Ages and has come to maturity in contemporary treatises on modern warfare. Major contributions have been made by philosophers as distinct and distinguished as Sun Tzu, Saint Augustine, Saint Thomas Aquinas, Francisco de Victoria, Francisco Suarez, Hugo Grotius and Immanuel Kant. The common thread has been the effort to reflect on and formulate the legitimate principles for determining the whys, whens and hows of war: Why should war be begun? When should war be declared? How should war be waged and terminated? Not surprisingly, in answering these questions, the philosophical field of military ethics is suitably contested This means that, as with any project of ethical reflection, the ‘just war’ tradition should be thought of as constantly evolving and changing, not as forever fixed and definitive. THE MILITARY TRADITION Whatever the case in love, all is not fair in war. And it is most decidedly not the case that anything goes. Military ethics is not beholden to or exhausted by the permissive mandate to win at all costs. The professional soldier or warrior is very much part of ‘a world of permissions and prohibitions – a moral world’. Even in the exacting conditions of warfare, military personnel are considered situated within a moral context that both empowers them and controls them: they are to act as professionals who place ethical honor above material, instrumental or territorial gain. Accordingly, as objectionable as war is, many military ethicists and commentators maintain that it can be waged in ways that can be evaluated as morally better or worse. Indeed, as in most realms of moral argument and action, the energizing imperative is that the more serious and extensive the effects of chosen actions, the more compelling the ethical justifications for them need to be.
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 | 0.005 | 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 | 0.009 | 0.034 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".