<i>Emotion and Virtue</i>, by Gopal Sreenivasan
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
What would a person look like if she were to possess a virtue like compassion or courage? This is the question that will come to mind when contemplating the haunting Giacometti painting, Portrait of a Woman (1965), on the cover of Gopal Sreenivasan’s book. Somewhat paradoxically, the inexpressive face and the static posture of the woman in no way suggest that emotions should play a role in virtue. What the hieratic figure does convey, however, is the importance of character and focus. The notions of character and focus are central in the moral psychology of virtues proposed by Sreenivasan. According to him, virtues such as compassion or courage consist in having character traits that allow agents reliably to focus their attention on the relevant moral features as well as on the actions that are called for. He holds that for a virtue to play this role, it has to be partly constituted by emotional traits. This account he takes to be true of a good number of virtues. He mentions generosity, kindness, benevolence, gratitude and patience, but compassion and courage are his prime examples. By contrast, Sreenivasan doubts that the virtues of justice and honesty can be treated in the same way. The claim that emotions play an important role in virtues is by no means a new one. Indeed, the claim that virtues involve emotional dispositions, which can be traced back to Aristotle, is prominent in contemporary virtue theory. What is original, however, is the exact role Sreenivasan attributes to emotions as well as the remarkably rich and sophisticated, indeed at times quite labyrinthine, arguments he offers for his account.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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