Friedrich Nietzsche, <i>On the Genealogy of Morals</i> and Criminology
Bibliographic record
Abstract
The writings of Friedrich Nietzsche have much to offer criminology. To date, however, his work has been largely neglected in this scholarship. Taking this lacuna seriously, this article reads Nietzsche’s second essay of On the Genealogy of Morals and explicates its importance to criminology. Specifically, focus is cast upon Nietzsche’s exposition of crime and particularly punishment, pertaining to the production of a calculating and calculable being upon whom pain and suffering can be inflicted and the ways that concerns over excesses of punishment come to be framed as problematic. Via this reading, it is claimed that On the Genealogy of Morals can serve, among others, as an important critique to many of the presuppositions that ground the classical school of criminology, epitomized in the work of Cesare Beccaria and Jeremy Bentham. The article concludes by locating the importance of Nietzsche to penology specifically and criminology more broadly.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".