Bibliographic record
Abstract
Abstract In the Middle Ages in western Europe mythography was intended to set out in clear narrative terms and explain the body of myth that might only be alluded to in the corpus of Classical Latin literature that was still read throughout this period. It might have had the further intention of refuting the error of pagan belief about the gods and rendering myth innocuous by subjecting its unseemly tales to interpretation, especially through allegory. Much of the substance and method of medieval mythography was based on the seminal work of Fulgentius and Isidore of Seville, who wrote at the close of the classical epoch and the opening of the Middle Ages. The work of mythography was done in larger encyclopedias, histories, and commentaries, but there were also specialist treatises, most notably, the books of the three Vatican Mythographers and Conrad of Mure’s Fabularius. Heroic myths were also recounted, as well as thoroughly adapted and modified, in the popular literature of the period, perhaps most prominently in Benoît de Sainte Maure’s French poem Le Roman de Troie and its Latin prose translation by Guido delle Colonne.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".