Bibliographic record
Abstract
In his Metaphysics Aristotle repudiates the poets’ claim that envy ( phthonos ) is natural to the divine nature, citing the adage that bards tell many lies ( i .2, 982b32–983a4). The repudiation is noteworthy: Aristotle does not normally deny passions to gods or quarrel with poets. Presumably, it is indebted to his view that envy is named in a way that involves badness, is a wicked passion, and something that is felt by those who are bad ( N.E . ii .6, 11078a8–13; E.E . ii .3, 1221b18–23; Rh . ii .11, 1388a34–36). Even so, his repudiation provokes several questions. What is Aristotle’s understanding of envy and its wicked nature? How does this sort of badness compare to and contrast with inappropriate realizations of passions such as anger and fear? How do passions relate to the character of those who feel them? Does (and, if so, how does) Aristotle’s understanding of passions’ inappropriateness in ethical matters fit with his understanding of their value elsewhere? The essay to follow investigates these questions, beginning with Aristotle’s most pervasive thoughts on passions’ inappropriateness, and their connections to character (section 1). The baseness of wicked passions is then explored (section 2), followed by an examination of envy (section 3) and its baseness (section 4). That and how envy suits Aristotle’s doctrine of the mean is considered (section 5), as is the inappropriateness and appropriateness of passions in diverse domains (section 6).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".