Bibliographic record
Abstract
It is my pleasure to be contributing this editorial for the Deakin Review of Children’s Literature.My name is Lynne Wiltse and I am an Associate Professor in the Department of Elementary Education at the University of Alberta in Edmonton, Alberta. I teach courses in language and literacy and children’s literature, and have eagerly read each issue of the Deakin Review of Children’s Literature since its inception in 2011. I have made sure to introduce my students to this valuable resource. For example, I have the undergraduate students in my children’s literature course undertake an assignment that requires them to explore the site in detail. With its feature editorial and reviews of contemporary English-language texts for children and young adults, the Deakin Review is an excellent resource for prospective teachers. This is the second time that the graduate students in my Children's Literature in the Elementary School (EDEL 510) course have participated by writing book reviews for a Special Issue of the Deakin Review (the reviews were published in Vol 4, No 4, 2015). The first time my students took part in this initiative was such a powerful learning experience that the next time I was due to teach the course, I approached the editorial team to see if we could recreate the opportunity. The result is this Special Issue. Once again, I am delighted with the diverse range of books my students selected for review. With the 2015 issue, the book review of The Right Word: Roget and His Thesaurus, introduced me to the amazing author and illustrator team of Jen Bryant and Melissa Sweet. I have since read all their other biographies written for children. This year, Ada Twist Scientist, written by Andrea Beaty and illustrated by David Roberts, one of the books selected for review, really caught my attention. I can’t wait to read Beaty’s other books! As most of my students are practicing elementary teachers, we focused on picture books for young children at the K-6 level. These graduate students were able to apply what they were learning about visual literacy and evaluating picture books in our course to the picture books they had selected to review. We also discuss the importance of authentic writing opportunities for children; writing the reviews certainly proved to be an authentic writing experience for my students, one that they took very seriously. We are excited to see the published reviews. I thank Kim Frail and the rest of the editorial team of the Deakin Review of Children’s Literature for their support of this special issue.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| 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 teacher head, 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".