Bibliographic record
Abstract
Dear Readers,As you can easily imagine, our journal receives regular deliveries from publishers of children’s books who would like to see their new titles reviewed by Deakin writers. For many years now, I have been pleased to open an increasing number of courier boxes filled with children’s books that raise awareness of diversity. The boxes keep coming and so do the books featuring diverse characters, including (but not limited to) LGBTQ youth, people with disabilities, ethnic and cultural minorities, and Indigenous peoples. The number of books written and illustrated by people from culturally, ethnically, and racially diverse backgrounds is also on the rise, and this circumstance bodes well for the future of children’s literature. Indeed, publishers appear eager to let their readers know that they take diversity seriously, and many publishers, such as Groundwood Books and Penguin UK, include a diversity statement or manifesto on their websites. This is good news that should be celebrated. Our fall issue is filled with thought-provoking books that embrace diversity, including Inuit Spirit (containing line drawings by world-renowned Inuit artist, Germaine Arnaktauyok), People of the Sea (describing the role of sea-people in Inuit culture), Isaac and His Amazing Asperger Superpowers! (helping children to understand the Asperger’s/autism spectrum), and many other compelling titles. These books help children appreciate differences in ethnicity, disability, culture, gender, lifestyle, and perspectives. If you read and enjoy a book about diversity, please spread the word and let other readers know about your discovery. We can all help to advocate for diversity in children’s literature by buying diverse books from booksellers and sharing them with young readers. Please spread the word that diversity matters!Best wishes,Robert DesmaraisManaging Editor
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.204 | 0.114 |
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".