Bibliographic record
Abstract
Abstract On an expressivist view, ethical claims are not fact stating; instead they serve the alternative function of expressing our feelings, attitudes and values. On a deflationary view, truth is not a property with a nature to be analyzed, but merely a grammatical device to aid us in endorsing sentences. Views on the relationship between expressivism and deflationism vary widely: they are compatible; they are incompatible; they are a natural pair; they doom one another. Here I explain some of these views, extract some necessary distinctions, and put these to use for understanding expressivism. I argue that contrary to the opinions of some, deflationism doesn’t help with problems of objectivity, knowledge and reasoning in ethics. I suggest alternative expressivist treatments of these problems, and show how expressivism as a metaethical view must have consequences for our ethical lives and beliefs. In particular it must affect the way we deal with ethical consistency—when norms or beliefs conflict—and ethical incompleteness—when ethical questions have no right answer.
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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.001 | 0.006 |
| 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".