Bibliographic record
Abstract
Although Donald Davidson is best known for his account of motivating reasons, towards the end of his life he did write about normative reasons, arguing for a novel form of realism we might call anomalous naturalism: anomalous, because it is not just non-reductive but also non-revisionary, refusing to compromise in any way on the thought that the prescriptive authority of normative reasons is objective and reaches to all possible agents; naturalism, because it still treats normative properties as perfectly ordinary causal properties, and thus avoids many of the epistemological problems that bedevil realisms of the sort recently advanced by Thomas Nagel, Derek Parfit, and T. M. Scanlon. In the first section of the paper, I discuss Davidson’s understanding of objective prescriptivity and one important challenge that it faces. In the second section, I show how an answer to this challenge can be found in Davidson’s holism of the mental. As we shall see, Davidson’s holism of the mental makes the possibility of strongly prescriptive properties much easier to take seriously. In the final section of the paper, I take up various grounds for doubting that such properties could also be causal.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| 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".