Bibliographic record
Abstract
Dear Deakin Readers,I hope you enjoyed a restful and happy holiday! Our editorial team met recently to develop a publishing plan for the coming year and we would like to communicate some important news. To keep everyone in the loop about our editorial plans and related activities, we will update our readers with an annual report, which we intend to publish at the end of each volume year. Thus, our first report will be published later this summer. On the matter of peer-reviewed issues, the editorial team will continue to solicit articles for review, so please consider contacting us if you are interested in writing a practitioner-themed scholarly article about children’s books (e.g., building a children’s science library; developing a young adult publishing program; writing picture books about children with disabilities; etc.). We intend to publish our second peer-reviewed article later this year, following on the success of our first peer-reviewed article in last year’s health issue (Vol. 4, No. 2).Finally, the April publication (Vol. 4, No. 4) will be a special double issue reserved for University of Alberta graduate students who are studying children’s literature, resource selection, and evaluation of children’s books in the Faculty of Education. We look forward to reading their thoughtful opinions and recommendations! In the meantime, we have recommended many good books to help you ward off the winter doldrums. Enjoy!Best wishes,Robert Desmarais, Managing Editor
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.314 | 0.269 |
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".