Bibliographic record
Abstract
Kerascoët. I Walk With Vanessa: A Story About a Simple Act of Kindness. Random House Children’s Books, 2018. This is a wordless picture book from French illustrators, Kerascoët. This husband and wife duo, Marie and Sébastien solely illustrate, without the use of text, the ability to combat bullying in modern society. They accomplish this difficult task by placing emphasis on the characters’ emotions through the use of distinct colour throughout the images, clearly depicting the story’s message. Because there are no words, this amazing resource provides students with the ability to interpret the book individually, creating unique perspectives such as an idea, "who else needs help other than Vanessa?" This book provides fresh insight into how society can unite together by creating a positive chain reaction when faced with bullying. Throughout the illustrations, this team accomplished this task extremely well, by providing the audience with diverse characters, creating a sense of belonging. This allows the reader to view the characters as if they were looking at their own reflection, seeing into their lives, therefore enabling them to relate to the book. With this, I truly believe that it is essential for children's books to act either as a window or a mirror for children. Overall, I feel that this book is ideal for a target audience of pre-kindergarten to grade two. The drawings are simplistic, with few details, allowing children to predict the storyline easily, leaving a thorough investigation of the book. I cannot wait for students to "read" this book to me. Highly recommended: 4 out of 4 starsReviewer: Terri Beach
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".