Bibliographic record
Abstract
Reading to children and storytelling has documented developmental benefits. Traditional Nursery Rhymes (Mother Goose tales in North America) encapsulate 'snapshots' of the people described and chronicle their customs, superstitions, and amusements. Art has long been employed to document the impact of human imperfections and diseases. We investigated whether illustrations accompanying nursery rhymes, suggest that any characters illustrated may have had or been based on recognized morphological abnormalities, and if this literature documents a role for grandmothers as storytellers. Archival materials were reviewed at the Victoria and Albert museum and Mary Evans picture library, and via the web. As early as 1695, Perrault included a frontispiece of a mature woman as storyteller in his book of fairytales. Similar scenes by various artists (Boilly, Cruikshank, Guy, Highmore, Maclise, Richter, and Smith) are found consistently from 1744 to 1908. Many illustrators (Aldin, Caldecott, Cruikshank, Doré, Dulac, Gale, Greenaway, Rackham, Tarrant, and Wood) portray infants, children, and adults who are dwarfed, giant, or whimsically grotesque. Many images certainly suggest genetic syndromes, and in some characters consistency of specific features is evident between artists. Our research confirms the wealth of children's nursery rhyme illustrations suggesting pathology; that an authoritative compilation of the morphologies depicted is lacking; and that historically, grandmothers have a central role as storytellers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".