Bibliographic record
Abstract
Shapiro, Sheryl, and Simon Shapiro. Better Together. Illus. Dušan Petričić. Toronto: Annick Press, 2011. Print. This concept book is infused with a cheerful, slightly mischievous spirit as the authors and illustrator explore a wide variety of, and a broad notion of, mixes. Kids might be familiar with many of the mixes—mixing cinnamon and sugar to spread on toast, mixing water and dirt to create mud, mixing blue and yellow to make green or mixing water and flour to create glue. Other mixes are more abstract—mixing up each team member’s different skills to make a great soccer team or mixing up musical instruments to make raucous music. Each mix is explained by a playful, rhyming poem that is easy-to-read and delightful to read aloud. The writers themselves decided to mix things up—the book is this husband-and-wife’s first collaborative effort and their obvious pleasure in wordplay is apparent. For example, the first poem, which explains what mixes are, contains the line “You stir and squoosh them, squish and moosh them” to create a brand new thing. The lively text is supported by the equally zippy illustrations. The first illustration is a highlight as Petričić shows how mixing the separate ingredients of ideas, paper, and drawings can produce a wonderful new thing: a picture book. The mix of vivid illustrations and energetic text has created a fun book that both kids and parents will enjoy. The book is recommended for children ages 4-7. 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.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.407 | 0.386 |
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".