The Motive of Duty, Emotional Motives, and the Kantian Criterion of Summonability
Bibliographic record
Abstract
ABSTRACT Kant notoriously privileges the motive of duty over other motives as uniquely capable of conferring moral worth upon our actions. When we look closely at the reasons he and his contemporary defenders offer for favouring the motive of duty, we find considerable confusion. When we take care to distinguish between the various criteria that are (sometimes only implicitly) invoked, we find that the case for the motive of duty's superiority falls apart. I show that with respect to one frequently invoked criterion, efficaciousness , the emotions fare no worse than the motive of duty—given their availability, it is within our power to will action from emotions no less than from the motive of duty. On examination, the Kantian case for the duty motive's superiority turns out to hinge on a different criterion: summonability . A motive satisfies this criterion only if it is within our power to summon the motive itself into our possession—to make it available to begin with. I offer the Kantian progressively more qualified interpretations of the claim that the duty motive is summonable and show that only the most enervated version of the claim survives, one on which the emotions and the motive of duty are on par.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".