MétaCan
Menu
Back to cohort
Record W4389199525 · doi:10.1017/s0003055423001247

“Filling the Ranks”: Moral Risk and the Ethics of Military Recruitment

2023· article· en· W4389199525 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmerican Political Science Review · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of WarwickNewcastle UniversityBritish AcademyUniversity of CambridgeHORIZON EUROPE Framework ProgrammeGovernment of the United KingdomUK Research and InnovationLondon School of Economics and Political Science
KeywordsArgument (complex analysis)State (computer science)Political scienceLaw and economicsPublic relationsLawSociologyMedicine

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.809
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.018
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.230
GPT teacher head0.388
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it