Developing Multiliteracies through Classical Mythology in British Classrooms
Bibliographic record
Abstract
In Britain, where classical studies has long been well established within education, a case study of some current uses of classical myth is presented. Arlene Holmes-Henderson considers the use of mythology as a different type of stimulus, namely to literacy, or, more accurately, multiliteracies, for primary school children in the United Kingdom. Although classical myth exists only on the fringes of the school curriculum, British children frequently discover the stories outside of the school framework, through reading, popular media, and informal education, and their attraction to the tales allows for exploitation for educational reasons, within a classroom setting. Presenting two case studies in which classical mythology was deliberately and creatively introduced to British classrooms, Holmes-Henderson demonstrates how the projects enhanced the development of multiliteracies in children aged seven to twelve.\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\nOpen Access of the complete 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.014 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".