Bibliographic record
Abstract
Shapiro, Sheryl and Simon Shapiro. Zebra Stripes Go Head to Toe. Toronto: Annick Press, 2013. Print.The truth must be stated: the cover of this title aroused false hope in one particular five-year-old. Selecting it from a proffered array of picture books, he cried, “I want a story about a zebra!” It wasn’t. Still, it was a beautifully illustrated introduction to geometric shapes and terms: squares, cubes, and (mostly parallel) lines. The Shapiros build concepts with colorful and distinct examples that are quite within the experience, real or vicarious, of children. We see building blocks, crosswalks, and, yes, the striped patterning of the zebra on the front cover. The text, a series of rhyming couplets, is both playful and informative. The font is very suitable for kindergarten or primary grades. Undoubtedly, the book can be used to develop a child’s spatial understanding and linguistic precision.What then to do about that not so small problem: the dashed hopes that arise when a book that promises to be about a zebra turns out to be a math text? Next time, this reviewer will preface its offering with a statement of fact: “This book can make arithmetic fun.”Recommended: 3 out of 4 stars Reviewer: Leslie AitkenLeslie Aitken’s long career in librarianship involved selection of children’s literature for school, public, special, and university collections. She is a former Curriculum Librarian at the University of Alberta.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.272 | 0.207 |
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".