Aristotle???s definition of non-rational pleasure and pain and desire
Bibliographic record
Abstract
“And what is natural is pleasant; and all pursue their natural pleasure.” (Aristotle, Historia Animalium ix .1, 589a8–9, trans. D. M. Balme) Aristotle thinks that it is not enough for us to simply know what virtue is, but that we also need to know how to bring virtue about. In order to do this, it is vital to have some insight into the non-rational mechanisms of human behavior, since, for Aristotle, the acquisition of virtue does not primarily consist in intellectual instruction, but in an adequate conditioning of our non-rational motivational dispositions. Before engaging in ethical debates, therefore, the young ought to be habituated in the right way such that they non-rationally desire and feel pleasure and pain about the right things. Non-rational pleasure and pain and desire thus provide a psychological mechanism which is central for the practical purpose of Aristotle’s ethical project. But what is this mechanism? This is the question I am going to investigate in this chapter. For Aristotle, such an investigation, since it is not concerned with rational behavior, falls into the domain of his natural philosophy. And it is from this perspective that I will approach the question here, too, namely from the perspective of Aristotle’s theory of animal behavior in his De Anima .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".