An inquiry concerning the place of emotions in virtue ethics (a comparison between Aristotle and Kant
Bibliographic record
Abstract
This dissertation examines the claim that, unlike utilitarianism and deontology, virtue ethics ascribes a positive role to emotions in moral evaluation by taking them as the constituents of moral goodness and moral value. I wish to identify the limit and scope of this claim and to show what kind of emotion theory is suitable for explaining the essential features of virtue ethics. To do so, I defend some kind of cognitivism, the cognitive-affective theory of emotion, as the most suitable theory for virtue ethics. I argue that the moral significance that virtue ethicists assign to emotions can only be explained by such a holistic and non-reductionist account of emotions. In order to demonstrate how the virtue ethicists̕ positive treatment of emotions with respect to moral evaluations works in theory, I have looked at Aristotle̕s theory of emotions and ethics, paying special attention to his notion of the ءmean relative to us.̕ We shall see that the ءmean relative to us,̕ which entails the existence of suitable emotions being felt by the moral agent, is justified on the basis of such an idea. The other main purpose of this dissertation is to examine whether Kant̕s ethics is compatible with virtue ethics. My interpretation is that Kant̕s position on emotions oscillates between the negative and the instrumentalist view, while Aristotle̕s view is moralist. I will argue that even the most celebrated Kantian feeling of respect does not fall under the moralist position. Although Kant recognizes emotions as morally relevant in the determination of duties of virtue, the kind of roles he assigns to them are merely aesthetic, instrumental, or ornamental and regulative, all of which are secondary to pure practical reason. But, in virtue ethics, emotions and feelings play actual causative roles. They can both influence and be influenced from reason in the determination of virtuous
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".