Bibliographic record
Abstract
Norman, Kim. Give Me Back My Bones! Illustrated by Bob Kolar. Candlewick Press, 2019. This book is a blend of fun and education. A pirate skeleton, whose bones have been spread across the ocean floor, wants to reclaim them. He “claim[s] his clavicle” and “hanker[s] for [his] humerus.” The text is a poem filled with surprising and creative descriptions of what the individual bones do: “Who can spot my shoulder blade, / my shrugging jacket-holder blade, / my shiver-when-I’m-colder blade? / Oh, scapula, come back!” The text is printed on Bob Kolar’s simple, bright, two-dimensional illustrations. There are some fun things to find in the illustrations. For example, when the pirate is looking for his hand-bones, we see them in the sand, hidden among hand-shaped corals. A squid returns his arm-bones. In some images fish peer at him suspiciously as he slowly collects his missing parts. As an educational work, this book is excellent. The front end papers show all of the disconnected bones with their names. The back end papers show the whole skeleton together with the bones named. Because it is a jaunty poem and fun to read, children will want to re-read it and will eventually memorize it. As a by-product of fun, they will learn what metacarpals and phalanges are. This book is highly recommended for pediatricians’ offices, as well as public and school libraries. Highly Recommended: 4 out of 4 starsReviewer: Sean C. Borle Sean C. Borle is a University of Alberta student in the Faculty of Medicine and Dentistry who is an advocate for child health and safety.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.455 | 0.473 |
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".