Seeing the Face, Seeing the Soul: Polemon's Physiognomy from Classical Antiquity to Medieval Islam
Bibliographic record
Abstract
Polemon of Laodicea (near modern Denizli, south-west Turkey) was a wealthy Greek aristocrat and a key member of the intellectual movement known as the Second Sophistic. Among his works was the Physiognomy , a manual on how to tell character from appearance, thus enabling its readers to choose friends and avoid enemies on sight. Its formula of detailed instruction and personal reminiscence proved so successful that the book was re-edited in the fourth century by Adamantius in Greek, translated and adapted by an unknown Latin author of the same era, and translated in the early Middle Ages into Syriac and Arabic. The surviving versions of Adamantius, Anonymus Latinus, and the Leiden Arabic more than make up for the loss of the original. \n \nThe present volume is the work of a team of leading Classicists and Arabists. The main surviving versions in Greek and Latin are translated into English for the first time. The Leiden Arabic translation is authoritatively re-edited and translated, as is a sample of the alternative Arabic Polemon. The texts and translations are introduced by a series of masterly studies that tell the story of the origins, function, and legacy of Polemon's work, a legacy especially rich in Islam. The story of the Physiognomy is the story of how one man's obsession with identifying enemies came to be taken up in the fascinating transmission of Greek thought into Arabic.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".