Studying Empowerment in English, French, and Persian Picture Books in the White Ravens Catalogues from 2015 to 2017
Bibliographic record
Abstract
The International Youth Library (IYL) in Munich publishes the White Ravens Catalogue every year in 25 languages in order to recommend good children’s and young adults’ books. Many adults, including librarians, parents, teachers, and also researchers, refer to this list to select books or to do research. The present study is based on catalogues from 2015 to 2017 in three languages: English, French and Persian. In this paper, after having introduced the concept of empowerment in children’s literature, I have analyzed this concept in original picture books published in these catalogues and subsequently rated them. Altogether I have closely read 53 picture books with fictional stories; I intentionally excluded non-fiction and poetry. Also, in order to clarify the process of evaluation, I have elaborated on three sample picture books. The main objective of this study is, therefore, to show how empowerment occurs (or does not) in White Ravens Lists in those years. The results, however, show that empowerment is more prevalent in English picture books than in French picture books, with Persian picture books less empowering. This paper defines aspects of empowerment in children’s and young adult literature with an ideal goal of calling attention to the concept of empowerment in judging and selecting books for children.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".