Bibliographic record
Abstract
Abstract A thorny question surrounding the meaning ofoughtconcerns a felt distinction between deontic uses ofoughtthat seem to evaluate a state of affairs versus those that seem to describe a requirement or obligation to perform an action, as in (A) and (B), respectively. (A) There ought not be childhood death and disease. (B) You ought to keep that promise. Various accounts have been offered to explain the contrast between “agentive” and “non-agentive”oughtsentences. One such account is the Agency-in-the-Prejacent theory (“AIP”), which traces the difference to a particular kind of ambiguity in the prejacent. This theory has been criticized as linguistically unviable. Indeed, I level a few novel complaints against AIP myself in the present paper. But AIP has a kernel of genuine insight which allows us to explain the contrast—that the distinction between agentive and non-agentiveoughtsentences owes in part to the way natural language encodes information about agency. I develop this idea into a novel account that, like AIP, traces the contrast to an ambiguity in the complement of the modal. However, according to the view I propose, the Coercion View, a linguistically-motivated coercion operation produces the necessary grammatical conditions for agentiveought, which in turn allow a kind of variadic function operator in the style of (in: Recanati, Literal Meaning. Cambridge University Press, 2004) to produce the semantic effect we see on display in agentive readings ofought. Having explained the mechanism by which we get this structure, I show that it corroborates some of the central intuitions underwriting agentiveought. I submit that the Coercion View offers an explanation of agentiveoughtto take at least as seriously as any of its competitors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".