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Record W2941199353 · doi:10.1111/phc3.12587

Collective harm and the inefficacy problem

2019· article· en· W2941199353 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhilosophy Compass · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicWar, Ethics, and Justification
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaConnaught Fund
KeywordsHarmsortPoint (geometry)Law and economicsComputer scienceEpistemologyRisk analysis (engineering)Political scienceBusinessSociologyLawMathematicsPhilosophy

Abstract

fetched live from OpenAlex

Abstract This paper discusses the inefficacy problem that arises in contexts of “collective harm.” These are contexts in which by acting in a certain sort of way, people collectively cause harm, or fail to prevent it, but no individual act of the relevant sort seems to itself make a difference. The inefficacy problem is that if acting in the relevant way won't make a difference, it's unclear why it would be wrong. Each individual can argue, “things will be just as bad whether or not I act in this way, so there's no point in doing otherwise.” The goal of this paper is to give an overview of some of the main responses available to the problem and to highlight central issues that arise for each type of response. In the final section, I explain what I take to be the most promising strategy and discuss the form that this strategy should take.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.951
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.060
GPT teacher head0.246
Teacher spread0.186 · 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