Bibliographic record
Abstract
Bayer, Vanessa. How Do You Care for a Very Sick Bear? Illustrated by Rosie Butcher. New York: Macmillan Publishing, 2019. Vanessa Bayer’s How Do You Care for a Very Sick Bear? provides young readers with advice on how to deal with and help their friends who are facing a difficult illness. The book offers suggestions and advice for young children, but also reminds them that even though their friend is sick, they are still their friend. Bayer’s story provides examples of the simple gestures that friends can make when helping each other. The illustrations by Rosie Butcher are bright, colourful, and simple. Butcher illustrates common activities that friends would do together, which makes them relatable to children even though the characters are bears. The illustrations take up most of the page and provide young readers with a lot to explore. The text throughout the book is simple and easy to read. Bayer’s story tackles a difficult topic, but she presents it in a way that is easy for children to understand. Her use of bears as her main characters helps to soften the impact of a difficult topic to approach with children. However, Bayer is also honest with her portrayal of illness, which offers children a realistic view of what to expect. This book can be very useful for children who have a friend facing a difficult illness. Additionally, it can be useful to parents when explaining illnesses to their children. With that in mind, I would recommend it for elementary school and public libraries. Highly Recommended: 4 out of 4 starsReviewer: Jenn Laskosky Jenn Laskosky is a masters student at the University of Alberta in the Library and Information Studies program. She has an interest in health sciences librarianship and international librarianship. Her passion for reading has continued to grow throughout her education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.048 |
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".