How "Ought" Exceeds but Implies "Can": Description and Encouragement in Moral Judgment
Bibliographic record
Abstract
This paper tests a theory about the relationship between two important topics in moral philosophy and psychology. One topic is the function of normative language, specifically claims that one “ought” to do something. Do these claims function to describe moral responsibilities, encourage specific behavior, or both? The other topic is the relationship between saying that one “ought” to do something and one’s ability to do it. In what respect, if any, does what one “ought” to do exceed what one “can” do? The theory tested here has two parts: (1) “ought” claims function to both describe responsibilities and encourage people to fulfill them (the dual-function hypothesis); (2) the two functions relate differently to ability, because the encouragement function is limited by the person’s ability, but the descriptive function is not (the interaction hypothesis). If this theory is correct, then in one respect “ought implies can” is false because people have responsibilities that exceed their abilities. But in another respect “ought implies can” is legitimate because it is not worthwhile to encourage people to do things that exceed their ability. Results from two behavioral experiments support the theory that “ought” exceeds but implies “can.” Results from a third experiment provide further evidence regarding an “ought” claim’s primary function and how contextual features can affect the interpretation of its functions.
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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".