Bibliographic record
Abstract
So far, we have understood practical reasoning as governed by the ideal of forming a legitimate general conception of the good. Most of the reasoning described so far seems completely teleological in character: A certain object appears to be good; we reflect on the adequacy of this appearance; we infer from the fact that this object appears to be good that other objects also appear to be good; and so on. Moreover, the good in question is an object of pursuit, the kind of thing that could be brought about in an action. For nonconsequentialists, this will seem like a serious strike against the theory; there seems to be no room in it, for instance, for deontological constraints. Concerns of this kind have made nonconsequentialist authors wary of the notion of good, and certainly of the notion of the good as something to be promoted or brought about. Scanlon, for instance, gives primacy to the notion of a reason and defends a “buck-passing” account of the good according to which “being good, or valuable, is not a property that itself provides a reason to respond to a thing in certain ways. Rather, to be good or valuable is to have other properties that constitute such reasons.” Moreover, Scanlon claims that various things we have reason to do, such as being good friends, cannot be understood as cases in which we have a reason to promote a certain good.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".