“Filling the Ranks”: Moral Risk and the Ethics of Military Recruitment
Bibliographic record
Abstract
If states are permitted to create and maintain a military force, by what means are they permitted to do so? This article argues that a theory of just recruitment should incorporate a concern for moral risk. Since the military is a morally risky profession for its members, recruitment policies should be evaluated in terms of how they distribute moral risk within a community. We show how common military recruitment practices exacerbate and concentrate moral risk exposure, using the UK as a case study. We argue that the British state wrongs its citizens by subjecting them to excessively morally risky recruitment practices. Since, we argue, this risk exposure cannot be justified by appealing to the benefits of a military career for recruits, our argument calls for reform of existing practices. Our method of evaluation is generalizable and therefore can be used to assess other states’ practices.
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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 | 0.035 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.051 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".