Bibliographic record
Abstract
Dear Readers,Our winter issue features many excellent book reviews that cover a fascinating range of subjects and experiences, such as: crossing a harbour to an extraordinary island full of treasure (The Riddlemaster), examining issues such as poverty, racism, addiction, and healing (Dreaming in Indian); and exploring the delightful flora and fauna of Australia (Simone in Australia). There are many more books to choose from and we hope you enjoy the variety.We also take great pleasure announcing that our new issue has book reviews from the recently updated Children’s Health Fiction Titles List, including: Fishing with Grandma, Mon ami Claire, Noni Speaks Up, Saila and Betty, and Tattle-tell. The update includes titles from 2014 to 2016 and we encourage readers to have a look at the full list, A Selective Collection of Children’s Health Fiction 2014 – 2016, in the University of Alberta’s Education and Research Archive (ERA).The Children’s Health Fiction list was created to help libraries and parents looking for high quality stories that help children to better understand and cope with health issues in their lives. Information about the project, a link to the original titles list, and guidance for selecting children’s fictional works on health-related topics, was published in the October 2014 Special Issue of the Deakin Review of Children’s Literature (Vol. 4, No. 2).On a final note in the way of announcements, we are delighted to offer a peer-reviewed article that “describes why and how the University of Alberta Libraries built a Spanish language children’s literature collection.” You will find it under the “Articles” heading of the Table of Contents. All of us at the Deakin Review wish you a peaceful and happy winter season filled with good books and many visits to the library.Best wishes,Robert Desmarais, Managing Editor (with thanks to Sandy Campbell & Maria Tan for an update on the Children’s Health Fiction Titles List)
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.645 | 0.643 |
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".