Bibliographic record
Abstract
Dear Readers, This issue marks the one-year anniversary of The Deakin Review of Children’s Literature, and our reviewers and editors are delighted to report that well over 100 English language books have been reviewed. We’re thrilled that so many reviews have been shared with our readers and although we’ve only been around for a year, we’re optimistic that our publication will be around for years and decades to come. Indeed, our user statistics continue to grow each month and it is fascinating to see that our publication has a growing international readership. As I look ahead to the next year I know we’ll continue to publish thoughtful reviews of new titles from picture books to teen fiction, and we’ll expand our offerings to include an occasional interview with an author or illustrator. We’re considering other changes like themed issues, guest editorials, and articles, but our raison d’être will always be to publish high quality book reviews. Our publication is distinctive in that our reviewers are all librarians, library staff, and educators from the University of Alberta, and thanks to this fine team we are able to serve our readers with so much thought-provoking content about the world of children’s book publishing. I wish you all a restful break now that summer is upon us and children are out of school, and I hope you share our excitement for the abundance of charming books in this issue. Happy reading! Robert DesmaraisManaging Editor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.193 | 0.324 |
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".