Bibliographic record
Abstract
Anstee, Ashlyn. Hedge Hog! Illustrated by Ashlyn Anstee. Tundra Books, 2018.After Are We There, Yeti? and No, No, Gnome!, Canadian born author/illustrator/animator Ashlyn Anstee presents us with the delightfully punny Hedge Hog!. In this story, our titular main character Hedgehog tries to keep all the other yard animals away from his hedge. Can the other animals convince him to open up his doors before winter comes? Not if Hedgehog has anything to say about it. The author tells a simple, yet charming story that can be used to teach a young reader about the importance of sharing and caring for your neighbours or as a political allegory dealing with immigration. Some readers will also enjoy the tale for what it is, a fun and entertaining story. The art is the real strong point of this story. The charming and pleasant looking characters, and the world of the yard that the author creates are sure to appeal to anyone reading through this book. Just the cover art alone is likely to pique anyone's interest. The illustrations are not only cute, but they also do a wonderful job of conveying the story. Regardless of the reader's reading level, they are sure to get something out of this tale.With strong, yet easily digestible writing and charming illustrations, this story is perfect for new readers. Whether they are reading by themselves or along with their parents, there is lots to fall in love with here.Highly Recommended: 4 out of 4 starsReviewer: Adam CohenAdam has his BSc in archaeology from the University of Calgary and is a current graduate student in the University of Alberta’s Masters of Library and Information Studies program. He is also a member of Future Librarians for Intellectual Freedom, and works as a metadata assistant at the University of Alberta Libraries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.743 | 0.677 |
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".