Artificial and Unconscious Selection in Nietzsche’s Genealogy: Expectorating the Poisoned Pill of the Lamarckian Reading
Bibliographic record
Abstract
I examine three kinds of criticism directed at philosophical genealogy. I call these substantive, performative, and semantic. I turn my attention to a particular substantive criticism that one may launch against essay two of On the Genealogy of Morals that turns on how Nietzsche answers “the time-crunch problem”. On the surface, there is evidence to suggest that Nietzsche accepts a false scientific theory, namely, Lamarck’s Inheritability Thesis, in order to account for the growth of a new human “organ”—morality. I demonstrate that the passages interpreted by some scholars to prove that Nietzsche is a Lamarckian can be reinterpreted along Darwinian lines. I demonstrate that Nietzsche hits upon the right drivers of phenotypical change in humans, namely, torture and enclosures (e.g., walls of early states), but misinterprets their true impact. Nietzsche believes that these technologies are responsible for producing what I call “culture-serving memory” and the bad conscience by causing emotions that once were expressed outwardly to turn inward causing the “psychological digestion” of the human animal. In reality, however, these mechanisms are conducive to breeding a particular type of individual, namely, one who is docile, by introducing artificial and unconscious selective pressures into the environment of early humans. In showing that Nietzsche’s genealogical account of memory and bad conscience is not underpinned on a false scientific theory and is consistent with Neo-Darwinism, I deflect a potentially fatal blow regarding the veracity of Nietzsche’s genealogies.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".