Bibliographic record
Abstract
Shannon, David. Bugs in My Hair! New York: Scholastic-The Blue Sky Press, 2013. Print.Bugs in My Hair! is an illustrated story book about head lice by acclaimed children’s author and illustrator David Shannon. This highly recommended book would be a good addition to any public, school, or health library interested in providing accurate but engaging information about head lice which is a common condition in the preschool and primary school age groups.The book sheds a humorous light on the not so funny topic of head lice through the experience of a young boy with this condition. Through this fictional account, Bugs in My Hair! portrays accurate medical information about head lice including method of transmission, symptoms, diagnosis, and cure. This book also dispels common myths about head lice, such as the ability to get head lice from animals, or using mayonnaise as a remedy. The story also depicts the social stigma and shame of having head lice and the feeling of being overwhelmed by the treatments, which would help a child understand the emotional experience or empathize with others who have head lice.The book has detailed and amusing illustrations such as the “lice-a-palooza” party on the host’s head. The age appropriate illustrations together with the hand drawn font keep the reader’s interest and charge the imagination.All in all, Bugs in My Hair!, is a fun and accurate read and a good way for children and parents to learn about head lice.Highly Recommended: 4 out of 4 starsReviewer: Connie WintherConnie is a Medical Librarian with Alberta Health Services. She has a broad interest in health care and medical librarianship. When not working, she enjoys all types of outdoor activities with her family
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.083 |
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".