A Less than Perfect World: Representation of Death in Award-Winning Picture Books
Bibliographic record
Abstract
This paper examines the presence of death in contemporary, 1990-2013, award-winning picture books in the United States, Canada, England, and Australia. Literary awards nationally, and globally, recognize titles that are considered to be exemplary in their genre. These awards--the Caldecott from the United States, the Governor General's Award for English-Language Illustration, the Kate Greenaway Medal from the United Kingdom, and the Children's Book Council of Australia's Best Picture Book of the Year Award--are the most prestigious in their country of origin and greatly impact the economic value and visibility of each title, the global children's book market, and the careers of the winning author and illustrators. Although death education for young children is hotly debated, its importance cannot be understated. Given the continuing trend of research showing the benefits of death education for children there has been a remarkable lack of award-winning literature that depicts death, dying, and learning how to cope with the extreme feelings these events create. A larger presence of death in award-winning children's literature would provide an easy-to-find, and reference, guide of excellent books parents or other adults could give to children to teach them about death, healthy coping methods, and empathy.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".