Bibliographic record
Abstract
This article contends that in crucial respects effective soldiers are ethical soldiers, that good soldiers in the military sense are good soldiers in the moral sense, and that this is so for quite traditional reasons. The thesis is defended by identifying and then resolving basic paradoxes regarding what soldiers must be trained to do or be, e.g.: be trained to kill but also not to be brutal; be trained to react in combat situations almost automatically but also to deliberate and decide if a command is unlawful; as peacekeepers, be trained to be impartial but also to know right from wrong and be firmly committed to upholding the former and opposing the latter. It is shown that contradictory things are not really thus being called for. With the aid of a blend of deontology and virtue theory, it is argued that certain standard qualities of effective soldiers have an associated moral dimension. For example, true military courage implies an unwillingness to engage in cruelty; the self‐control on which success of missions depends implies eschewing motives of personal vengeance; and the capacity for comprehending complex equipment and data implies a mentality for assessing the validity of orders.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".