Bibliographic record
Abstract
Abstract There has been little discussion of strict akrasia in contemporary literature on Stoicism ever since Brad Inwood (1985.Ethics and Human Action in Early Stoicism. Clarendon Press; Oxford University Press) persuasively argued that Stoic psychology has no means to account for such a phenomenon. And it is true that we find no such phenomenon in Epictetus. However, Inwood’s argument only applies to akrasia in the strict sense, which is when an agent knowingly acts contrary to a self-directed imperative. Stoicism can still allow for akrasia in the broad sense, which is defined by Inwood as any instance when “an agent fails to stand by a previous decision about what he will do or by some general plan or programme of action” (133). When we widen our conception of akrasia to include the broad sense it becomes apparent that this phenomenon is of significant importance to Epictetus. This is best made evident through analogy with Aristotle. This paper argues that Epictetus’ ethics involves three key features which are also present in Aristotle’s discussion of akrasia in the Nicomachean Ethics: 1) A major problem for agents is when they fail to render a universal premise effective at motivating a particular action in accordance with that premise. 2) There are two reasons this occurs: Precipitancy and Weakness. 3) Precipitancy and Weakness can be prevented by gaining a fuller understanding of our beliefs and commitments. This comparison should make clear that akrasia is certainly not absent from Epictetus. Rather a very Aristotelian understanding of why we fail to act in accordance with what we take to be in our own best interests remains at the center of his ethics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".