‘Emotions’ in Gopal Sreenivasan's <i>Emotion and Virtue</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In his remarkable new book, Emotion and Virtue , Sreenivasan defends the view that, in the case of many virtues, in order for an exemplar of each of these virtues to be a reliable judge of what that virtue requires in specific circumstances, she must possess a particular, morally rectified, emotional trait. In this article, I raise two challenges to “the argument from salience” that Sreenivasan offers in favor of this view. First, I argue that, although Sreenivasan wishes to remain neutral about different philosophical theories of emotions, the success of his argument depends, in fact, on the outcome of the debate about the nature of emotions. Second, I challenge the central claim of Sreenivasan's argument from salience, namely, that the possession of a morally rectified emotional trait, cleverness, and supplementary moral knowledge is sufficient to explain an agent's ability to reliably judge what a given virtue requires in specific circumstances.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it