Bibliographic record
Abstract
Arsenault, Isabelle. Collette’s Lost Pet. Tundra Books, 2017.In this attractive, graphic novel-style picture book, Canadian author and illustrator Isabelle Arsenault tells the story of Collette—the new girl at Mile End. Unsure of how to meet other children in her new neighbourhood, Collette invents a story about a lost bird. As more children are recruited to help search for Collette’s lost bird, the fib grows, too. Although children and adults of all ages will enjoy this book’s delightful illustrations, the text is best suited to more mature readers within and above the publisher-recommended age range of 4 to 8 years. Since many younger children are taught to always speak truthfully, this book is more suitable for older readers who can reflect critically on a white lie that is not punished, but instead elicits empathy and kindness. Additionally, much of the story is told through Arsenault’s illustrations, which invite readers to find meaning beyond the words on the page. However, this nuanced relationship between text and illustrations is balanced by familiar picture book conventions, such as the repetition of phrases.Collette’s Lost Pet explores the relatable themes of being new and trying to fit in, and the protagonist’s fanciful invention of an increasingly larger-than-life parakeet makes this book’s text as engaging as its illustrations. This story demonstrates the importance of welcoming newcomers and is sure to be an appreciated addition to school, public, and home library collections.Highly Recommended: 4 out of 4 starsReviewer: Samantha NugentSam works as a librarian at the Hinton Municipal Library.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.097 | 0.050 |
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".