Bibliographic record
Abstract
I advance an approach to thinking about moral obligation and how it moves us that runs counter to mainstream thought in ethics. Many assume, with Kant, that bona fide moral obligation must involve some truly unconditional, categorical, or inescapable constraint. Following in Hume’s footsteps, I advocate for viewing our paradigmatic obligations as instead deriving from rules of important social practices, followed out of a felt sense of reverence or regard. I do not offer a complete defense of this more Humean approach here; instead, I respond to a familiar kind of objection. Some allege that seeing our obligations this way can lead to a troubling kind of conflict between an agent’s motive to fulfill her obligations and the very values that, for her, make a given practice and its rules worthy of her devotion. In such circumstances, the objector alleges, any motive to fulfill her obligations will amount to little more than an agent’s “worshiping” of the rules. I disarm this charge of “rule-worship” as it has been levied against the Humean approach, though not by denying that this sort of motivational conflict can arise. Instead, I argue that once the source and nature of conflict are better understood, its possibility is no threat. Indeed, far from a strike against it, the way that the Humean approach to obligation helps us to make good sense of this kind of conflict is ethically attractive, and thus a selling point.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.044 | 0.014 |
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".