Bibliographic record
Abstract
Lionni, Leo. Little Blue and Little Yellow. New York: Alfred A. Knopf, 2011. Print. Award-winning author, artist, and graphic illustrator, Leo Lionni, had a distinguished, decades-spanning career and wrote over 40 children’s books in an easily recognizable style. Little Blue and Little Yellow was his first children’s book, and it won the New York Times Book Review Best Illustrated Children’s Book of the Year award in 1959. Lionni was also a four-time Caldecott Honor Book winner, an award that celebrates excellence in children’s picture books. This review pertains to the 2011 board book edition, just right for the littlest hands. The story is simple perfection. Little Blue and Little Yellow, are best friends who live across the street from one another. They enjoy all sorts of games both together and with their other equally-colourful friends. One day, Little Blue wants to play with Little Yellow but cannot find him. Overjoyed as they finally meet up, they hug until they become green! However, when they go home, their parents do not recognize them, and they are very sad. Where did Little Blue and Little Yellow go? Are they lost? This delightful story has many layers. It can simply be read as a way to introduce the concept of colour to young children, but it has deeper, yet understated, themes of friendship and diversity. It is a delight to read and look at, and while this sturdy edition is certainly aimed at the preschool crowd, older children will enjoy it too. Highly recommended: 4 out of 4 starsReviewer: Debbie FeisstDebbie is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. When not renovating, she enjoys travel, fitness and young adult fiction.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 0.106 |
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".