Bibliographic record
Abstract
Habermas charges that Foucault's work `cannot account for its normative foundations'. Responses to Habermas have consisted mostly of, on one hand, attempts to identify foundational normative assumptions implicit in Foucault's work, and, on the other hand, attempts to show that Foucault's work discredits the very idea of normative foundations. These attempts have suffered from a lack of clarity about Habermas' notion of normative foundations. In this article I clarify the terms of the debate by considering Habermas' critique of Foucault in light of his moral philosophy. I examine three representative responses to Habermas on Foucault's behalf, which attempt to identify normative foundations in Foucault's work, and I show why none of them meets Habermas' requirements. Finally, I argue that while Foucault's political judgments cannot have normative foundations, Foucault does adhere to the principles of Habermas' discourse ethics, and his doing so does not conflict with his genealogical approach.
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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 | 0.051 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.073 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 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".