Bibliographic record
Abstract
Why does Bernard Stiegler speak of “this culture, which I have named, after Epictetus, my melete?” In the first part of this article, I elucidate Stiegler’s claims about both Stoic exercises of reading and writing and their significance for the interpretive questions he has adapted from Michel Foucault and Jacques Derrida. In particular, I address the relations among care for oneself and others, the use of material technologies, and resistance to subjection or “freedom.” In the second part, I consider the merits and limitations of Stiegler’s comments about reading and writing in Stoicism, with particular attention to Epictetus. We will see that Stiegler’s interpretive frame-work casts considerable light on ancient texts and contexts, on the condition that it be combined with close reading of ancient texts and engagement with specialist scholarship. Finally, in the conclusion, I will suggest that the history of technology in Epictetus’s time contributes to a debate about Stiegler’s theories.Bernard Stiegler signale à plusieurs reprises l’importance des exercices stoïciens de lecture et d’écriture. Dans la première partie de cet article, j’essaye de clarifier ces assertions et d’expliquer leur lien aux oeuvres de Michel Foucault et de Jacques Derrida. Il s’agit en particulier des rapports entre le souci de soi et d’autrui, l’usage des techniques et des matériaux et la résistante à la soumission ou à la « liberté ». Dans la deuxième partie, je considère les mérites ainsi que les limites des remarques de Stiegler sur la lecture et l’écriture au sein du stoïcisme, en portant une attention particulière à Épictète. Le point du vue stieglerien donnera de nouvelles significations à quelques passages des oeuvres d’Épictète, à condition qu’il soit conjugué à une lecture attentive d’études spécialisées et de textes anciens. Je conclurai, dans la troisième partie, en proposant que l’histoire des techniques à l’époque d’Épictète pourrait alimenter un débat à l’égard des théories de Stiegler.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".