Bibliographic record
Abstract
In this paper, I argue that purely quantitative understandings of Aristotle's concept of "the mean" (as presented in Nicomachean Ethics) are oversimplified, and I make this argument by analyzing the particular emotion of anger. Anger, I contend, helps to complicate the purely quantitative understanding of the mean, insofar as, I argue, the amount of anger experienced is not the morally salient feature in determining whether or not the anger is virtuous. Rather, anger is one example of an emotion or trait for which other, non-quantitative parameters of the mean are more salient, giving us a more nuanced understanding of what the mean is. Anger is virtuous not when it is in the right measurable degree, but rather when it is directed at the proper target. In this way, the virtue-making property of anger is distinctly qualitative. Examining anger provides insight into the concept of the mean and its role in Aristotle's ethics, and also helps to shed light on contemporary debates about political anger.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".