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Record W2929487516 · doi:10.5325/jnietstud.50.1.0033

“Being Just Is Always a <i>Positive</i> Attitude”: Justice in Nietzsche's Virtue Epistemology

2019· article· en· W2929487516 on OpenAlexaff
Rachel Cristy

Bibliographic record

VenueThe Journal of Nietzsche Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicNietzsche, Schopenhauer, and Hegel
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVirtueEconomic JusticeEpistemologyPhilosophyEpistemic virtuePerspectivismPoliticsPraiseInterpretation (philosophy)DoctrineTheologyLawPsychologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

ABSTRACT In his early-middle works, notably in the Untimely Meditations and Human, All Too Human, Nietzsche uses the concept justice (Gerechtigkeit) in an unusual way: he treats it as an individual virtue rather than a structural virtue of societies and political or legal systems; he distinguishes between practical and epistemic justice, and focuses his analysis and praise almost exclusively on the latter; and he places unusual emphasis on the distinctive feeling or affect associated with the exercise of epistemic justice. This article aims to make sense of Nietzsche's peculiar conception of justice in by contextualizing it in two respects. First I show how it fits into and supports an interpretation of Nietzsche as a virtue epistemologist. Then I show how elements of Nietzsche's epistemic and affective notion of justice persist in his later work, notably in his doctrine of perspectivism, in the more straightforwardly practical and political account of justice sketched in the Genealogy, and his conception of the role of the philosopher of the future.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.045
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.046
GPT teacher head0.295
Teacher spread0.249 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2019
Admission routes1
Has abstractyes

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