Bibliographic record
Abstract
The idea of this book is to closely examine all passages where Socrates talks about the Delphic precept, ‘Know Thyself’, and see what picture of self-knowledge emerges. Given that Socrates is a key figure in the transmission of this precept, it is very likely that such a project leads to significant results. After a discussion of the inscription itself, Moore discusses the relevant passages in the Charmides, the Alcibiabes I, the Phaedrus, the Philebus, Xenophon's Memorabilia, and other texts. A detailed discussion of the Apology is missing; however, it might have been helpful. There are far more things to be praised than questioned in the book. In its details, it is rich, well crafted, and largely convincing. The reader of this review should keep this in mind while I concentrate on a couple of things I am less than fully satisfied with. Socratic self-knowledge is not simply a sort of introspective, first personal knowledge (p. 2). Moore captures this by arguing that it amounts to self-constitution (p. 140). He is right, but I think his insight is compromised in three ways.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".