Bibliographic record
Abstract
Abstract Byzantine education was based on a reading of Homer, and so mythological themes were of perennial interest to the learned society of Byzantium. Byzantine scholars had access to the ancient works of systematic mythography, but they also made their own contributions to the understanding of myth, even if not in a distinct genre of mythography. They had been taught by St. Basil that moral instruction might be found in myth, and by Eusebius of Caesarea that the gods of myth were defunct kings. So, much of the common stock of Byzantine mythographic knowledge was contained in the chronicle tradition, and practically all of it can be traced back to the chronicle of John Malalas. His first five books are preoccupied with what must strike us as odd and idiosyncratic stories of gods and heroes, including a particularly extensive and influential account of the Trojan War, but they were the norm in Byzantine literature. Mythographic material might also be found in lexica and encyclopedias, poetic descriptions of statuary, the lore of Constantinople, and even the Dionysiaca of Nonnus. John Tztetzes and Eustathius, in what amounted to Homeric commentaries, gave a historical background to Homer’s work indebted to Malalas and employed allegory to tease out deeper meanings in the epics. The later Byzantine centuries cultivated a poetic tradition that returned to Homer and Troy—with varying degrees of faithfulness—for its plots, characters, and themes, and even found room for translations of French treatments of the matière de Troie.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".