The Illustrated Page: Analyzing Illustrations of HistoricalChildren’s Books Using Citizen Science
Bibliographic record
Abstract
This paper presents the first large-scale, systematic study of historical children’s book illustrations through a combination of citizen science and computational analysis. Using a corpus of 27,901 digitized illustrations from 2,827 books from the Internet Archive’s Children’s Library, we developed a structured annotation workflow deployed on Zooniverse to collect over 400,000 annotations from 902 volunteers. Tasks included identifying depicted characters, objects, settings, and emotional tone. We assess inter-annotator reliability across task types and derive consensus labels to explore three central questions: who and what is most commonly visualized, which entities co-occur, and how visual depictions change over time. Findings reveal dominant portrayals of patriarchal figures and animals, the centrality of nature, and gendered patterns in emotional framing. Temporal analysis shows a surprising visual stability over 140 years. This work demonstrates the value of human-in-the-loop annotation for visual cultural heritage and provides a new resource for studying the visual language of childhood in print.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".