Bibliographic record
Abstract
Dear Readers,I had lunch the other day with a colleague who told me of her interest in doing research about an obscure and forgotten author from long ago who wrote and published a novel at the age of twelve. How fascinating, I thought, that this young girl was inspired and determined to submit her manuscript to a publisher in the 1920s, a time when few children could call themselves published authors.But what kind of support exists today for young scribblers? Perhaps not surprisingly, it all begins with you, whether you are a parent, aunt, uncle, teacher, librarian, or adult friend of a child, all of you can make a difference by encouraging children to read and write. You can also let children know about online resources devoted to helping young authors develop their writing and illustration skills. For example, I discovered Scribblitt.com, which is a terrific website where kids can use free online tools to write and illustrate their own stories; they also have the option of collaborating with other kids and writers using cloud-based technology. Furthermore, you can help children to develop their writing skills by proofreading their stories and offering them helpful advice about spelling, character development, narrative structure, and so on.Another way for children to get inspired about reading and writing is to check the websites of their favourite authors, which are generally chock-full of activities and information about children’s writers and illustrators. To wit, I had the great pleasure of recently meeting and interviewing Jill Bryant, a Canadian writer who specializes in children’s nonfiction, when she was visiting the University of Alberta and meeting with numerous groups of children, inspiring them to read and write. Her website, Jillbryant.ca, has some excellent teacher resources that encourage students in grades four to eight to write about their role models, using her books about real entrepreneurs, athletes, and designers for inspiration. When you begin looking at author websites and other online resources (e.g., directories, readers’ advisory services, webcasts, etc.) for information about writing children’s books, it becomes readily apparent that there is a plethora of writing support tools for budding young authors.Enjoy the summer issue, and please take note that we have also included a review in French of a French language book for children. We are delighted to announce that the Deakin Review will continue to review books in French as our resources permit.Happy reading!Robert DesmaraisManaging EditorClick here to watch the interview with Jill Bryant.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.267 | 0.311 |
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".