Bibliographic record
Abstract
Notes on Contributors. Editor's Introduction. 1. The Literary and Philosophical Style of The Republic: Christopher Rowe (University of Durham). 2. Allegory and Myth in Plato's Republic: Jonathan Lear (The University of Chicago). 3. Socrates' Refutation of Thrasymachus: Rachel Barney (University of Toronto). 4. Plato's Challenge: the Case Against Justice in Republic II: Christopher Shields (Oxford University). 5. The Gods and Piety of Plato's Republic: Mark L. McPherran (University of Maine). 6. Plato on Learning to Love Beauty: Gabriel Richardson Lear (The University of Chicago). 7. Methods of Reasoning About Justice in Plato's Republic: Gerasimos Santas (University of California, Irvine). 8. The Analysis of the Soul in Plato's Republic: Hendrik Lorenz (Princeton University). 9. The Divided Soul and Desire for the Good in Plato's Republic: Mariana Anagnostopoulos (California State University). 10. Plato and the Ship of State: David Keyt (University of Washington). 11. Knowledge, Recollection and the Forms in Republic VII: Michael T. Ferejohn (Duke University). 12. Plato's Theory of Forms in the Republic: Terrence Penner (University of Edinburgh). 13. Plato's Defense of Justice: Rachel G. K. Singpurwalla (Southern Illinois University). Bibliography. Index
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.147 | 0.074 |
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".