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Record W2588959484 · doi:10.1111/phpr.12369

What Makes a Person Liable to Defensive Harm?

2017· article· en· W2588959484 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhilosophy and Phenomenological Research · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsQueen's University
Fundersnot available
KeywordsWrongdoingHarmLiabilityLaw and economicsLuckRecklessnessMoral responsibilityControl (management)LawBusinessEconomicsPhilosophyPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

On Jeff McMahan's influential ‘responsibility account’ of moral liability to defensive killing, one can forfeit one's right not be killed by engaging in an ordinary, morally permissible risk‐imposing activity, such as driving a car. If, through no fault of hers, a driver's car veers out of control and toward a pedestrian, the account deems it no violation of the driver's right to save the pedestrian's life at the expense of the driver's life. Many critics reject the responsibility account on the grounds that, first, it has draconian implications for threateners like the driver, and second, it contravenes the plausible principle that wronging one's victim is necessary for forfeiting one's rights. But I argue, drawing on the account's luck‐egalitarian underpinnings, that (1) the account lacks the draconian implications widely attributed to it, and (2) contrary to what many assume, wrongdoing is unnecessary for rights‐forfeiture. Via these arguments, I seek both to deepen our understanding of the responsibility account, and to reissue it in a more plausible and attractive form.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.632
GPT teacher head0.433
Teacher spread0.199 · 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