Bibliographic record
Abstract
North, Laura. Hansel and Gretel and the Green Witch. Illus. Chris Jevons. St. Catharines, ON: Crabtree Publishing, 2015. Print.This book is a “health message” twist on the Hansel and Gretel story. The children in this story watch television and eat junk food. They follow a trail of doughnuts into the forest, where they are captured by a witch. Instead of fattening them up, she forces them to do exercise and eat healthy foods, because she only eats healthy kids. By the time the witch, who sometimes wears sweat pants and carries a megaphone, thinks Hansel and Gretel are ready to eat, they are fit enough to run away. Children will like the brightly coloured pictures, which the illustrator, Chris Jevons, obviously had fun creating. They will also be drawn in by Laura North’s completely original take on the story that they already know. The story is simply told, with easy words for beginning readers. The two puzzles at the end of the book are not essential to the story, but would be a fun way to review the story with children. This book is recommended for school and public libraries.Highly Recommended: 4 stars out of 4Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.035 |
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".