History, Genealogy, Nietzsche: Comments on Jesse Prinz, “Genealogies of Morals: Nietzsche's Method Compared”
Bibliographic record
Abstract
ABSTRACT Jesse Prinz contrasts Nietzsche's way of historicizing morals with the approaches of utilitarians and Marxist-materialists, and does so to good effect. Against a background of substantial agreement on most of what Prinz argues for, I elucidate a significant shortcoming of his interpretation of Nietzschean genealogy: namely, its reliance on a simplistic understanding of how and why Nietzsche integrates historical hypotheses about how morality emerged and developed with critical warnings about where it seems to be headed. Using Nietzsche's teasing remarks about “the English psychologists” in the opening sections of GM I as a foil, I develop an account of the difference between a simple history of morals and a true, Nietzschean genealogy of morals that does a better job than Prinz does here of achieving his stated goal of identifying the advantages of Nietzsche's genealogy of morals over the utilitarian and Marxist-materialist versions.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".