“The Greatest Stories Ever Told”: US Classical Mythology Courses in the New Millennium
Bibliographic record
Abstract
Emily Gunter and Dan Curley carried out a survey of the 3,000 or so myth courses run by colleges in the United States, contacting them and subsequently receiving 589 syllabi in response. From this information, they created a database, examining which departments offer myth courses; the structures of the courses themselves; which Graeco-Roman gods, heroes, and myths are taught; and what themes and motifs are addressed. Analysing this information, they were then able to discuss some current and emerging trends, such as the use of screen media and gaming, as well as the increasing utilization of trigger warnings with regard to gender, sexuality, and violence. Such elements appear to define, or have the potential to define, the twenty-first-century mythology classroom in the United States.\n\nThe complete volume "Our Mythical Education: The Reception of Classical Myth Worldwide in Formal Education, 1900–2020", edited by Lisa Maurice, focuses on school education including a wide geographical and chronological range. The volume covers Eastern and Western Europe, Asia, Africa, the Americas (including Canada, the USA, and South America), Australia and New Zealand.\n\nPublished in the series “Our Mythical Childhood”, edited by Prof. Katarzyna Marciniak, Faculty of “Artes Liberales”, University of Warsaw, Poland.\n\nGold Open Access of the whole volume is available at https://www.wuw.pl/product-eng-14887-Our-Mythical-Education-The-Reception-of-Classical-Myth-Worldwide-in-Formal-Education-1900-2020-PDF.html
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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".