Bibliographic record
Abstract
This article examines genealogical investigations in an attempt to explain what they are, how they work, and what purpose they serve. It is a critique of Robert Brandom’s view of genealogists as naïve semanticists who believe that normative thinking, as it relates to all forms of epistemic inquiry and language use, is reducible to naturalistic causes. This reduction, Brandom claims, is hopelessly misguided and semantically incoherent since genealogies are not epistemically neutral in that “they count no more and no less,” as Habermas put it, than the traditional accounts of moral phenomena that they seek to replace. Nietzsche’s “story” of the origin of guilt in On the Genealogy of Morals is thus not even a useful fiction but merely meaningless. My analysis shows that Brandom’s understanding of genealogy is rather simplistic. While genealogists are naturalists insofar as they attempt to discover the specific historical causes that gave rise to current normative practices, they are neither reductive empiricists nor first-stage semanticists, as Brandom calls them, but multidimensional power-pragmatists.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".