Bibliographic record
Abstract
Dear Readers,A couple of months ago I had an opportunity to sit down with children’s literacy advocate Joyce Grant and discuss her popular Gabby series of picture books (click here to watch the video). I was really pleased to have the chance to meet with Grant, especially because two of her popular books, Gabby, Drama Queen and Gabby Wonder Girl, were favourably reviewed by Deakin reviewer Leslie Aitken. I was eager to pursue some questions inspired by Aitken's thoughtful comments, especially her description of Gabby, Drama Queen's plot as “imaginative and complex.” Naturally, I was optimistic that I would enjoy reading the entire Gabby series prior to the interview. Now that I have read the series, I can assure readers that the books are delightful, and teachers will certainly appreciate the teacher’s guide that is freely available online to help elementary students to develop reading, writing, and comprehension skills. I met with Grant at the University of Alberta which was one of her stops on an ambitious tour of Alberta’s schools and libraries to celebrate books and reading during TD Canadian Children’s Book Week. She was scheduled to speak at our institution about her experience teaching kids how to spot fake news, and I was glad to hear that she actively encourages kids to read news on her website teachingkidsnews.com that publishes free daily stories for young readers. The website has lots of important stories that are worth talking about and debating, and as we can all attest, kids need to know how to differentiate between real news and stories that deliberately mislead readers for financial or political gain. Grant also publishes a blog called Getting Kids Reading (gkreading.com) that is chock-full of articles, games, crafts, and ideas to inspire kids to read.Grant was a pleasure to interview, and I hope you will take time to watch the video. Aside from answering several questions about the Gabby series, Grant also tells us about her latest projects and how to get in touch. Our new summer issue is filled with many excellent book recommendations, including some reviews of multimedia resources for kids that resulted from a student assignment in the Multimedia Literacies course offered at the University of Alberta’s School of Library and Information Studies.Wishing you all a wonderful summer!Best wishes,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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.008 | 0.033 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".