Bibliographic record
Abstract
The Stoic Epictetus famously criticizes his students for studying Stoicism as ‘mere theory’ and encouraged them to add training to their educational program. This is made all the more interesting by the fact that Epictetus, as a Stoic, was committed to notion that wisdom is sufficient to be virtuous, so theory should be all that’s required to achieve virtue. How are we then to make sense of Epictetus criticism of an overreliance on theory, and his insistence on adding training? This paper argues that this tension can be resolved through an appeal to the metaphor of ‘digesting theory’. Epictetus discusses the digestion of theory in three parts of his existent work. While the use of digestion as a metaphor for moral progress in Epictetus has been noted, an explanation as to exactly what this process consists of has yet to be provided. This paper attempts to provide such an account. I argue that digestion consists of assimilating what we have learnt conceptually, at the level of general principles, into specific beliefs concerning existent objects. I argue further that this process of digestion can only be achieved through what Epictetus calls training (askesis).
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".