Bibliographic record
Abstract
Inglis, Kate. If I were a Zombie. Illustrated by Eric Orchard. Nimbus Publishing, 2016.This book is a collection of poems written and illustrated by Canadians. Each poem introduces the reader to a new creature or monster with a picture that looks drawn by a child. The poems detail how the creature or monsters would behave through the mind of a child. Some examples of the monsters or creatures in this book are a giant, vampire, alien, goblin, mermaid and a zombie. The rhythmic prose of each stanza adds to the playful nature of each creature.The illustrations in this book are bright and vibrant with bold colours such as green, brown and blue. They are consistent and an integral part of the picture book, providing visual support for the text. The illustrator’s use of line and alternating black and white text, add to the mysteriousness of these creatures.I immediately picked up this book because of the captivating jacket design with the large, inviting title and quirky zombie picture. Yet the jacket design, both the cover and the teaser, misled me to believe that the book would be about zombies. Rather, the theme of the book revolves around a child’s imagination and what they would do if they were a certain creature or monster. The content of the book would be very enjoyable as a read aloud for a younger child, yet some text may be not easily understood by children of any age unfamiliar with our North American culture with words used such as kayak, Frisbee or cauldron.Recommended: 3 out of 4 starsReviewer: Tina VossTina is an elementary school teacher currently pursuing a Master’s degree in Education. When she is not reading any book she can get her hands on, she is walking her dog Phill.
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 | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.114 | 0.075 |
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".