Bibliographic record
Abstract
Yoon, Salina. Who Do I See? New York: Random House, 2011. Print. Identifying five adorable animals and remembering five eye-catching colours are the challenges for toddlers and preschool children in Yoon’s book, Who Do I See?. The colourful illustrations are simple two dimensional cartoon drawings of familiar creatures including a fish, a tiger, a turtle, a ladybug, and a parrot. The illustrations of the animals are framed, and partially hidden, by cleverly placed cut out windows. The cut out windows allow readers to feel the shiny foil on the animal illustration such as scales on a fish, and, because the rest of the animal is hidden until the reader flips the page, children can try identifying the animal by thinking about common animal colours and shapes such as the black spots and red wings found on a ladybug. The text does not rhyme but playful repetition of the question and of the typical colours found on these animals will keep children engaged. The thick cardboard pages ensure that the book is not easily damaged. This interactive, guessing-game book will assist children in learning about different animals and different colours. Recommended: 3 out of 4 stars Reviewer: Tami Oliphant Tami works as a research librarian at the University of Alberta Libraries and for the School of Library and Information Studies at the University of Alberta. She earned her Master of Library and Information Studies from the University of Alberta and her doctorate from the University of Western Ontario. She has worked in academic libraries, public libraries, communications and planning, and as a sessional lecturer and researcher at the University of Alberta and the University of Western Ontario.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.266 | 0.317 |
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".