Bibliographic record
Abstract
Bently, Peter. The Great Sheep Shenanigans. Illus. M. Matsuoka. London: Andersen Press, 2011. Print. In this tale, it is really the wolf, Lou Pine (hear the French loup and Lupin, the werewolf from Harry Potter), who gets up to shenanigans, rather than the sheep. The text is a clever and funny rhyme that tells the story of the wolf trying to catch a lamb to eat. Along the way, we hear sheep-related cultural and literary references from, “the wolf in sheep’s clothing,” to Red Riding Hood. “I’m just in the mood for a Gran-flavoured snack,” the wolf tells us. The wolf makes many attempts to catch a lamb, but is always thwarted, if not by his own silliness, then by the water-gun toting Ma Watson, by bees, by Red Riding Hood’s Granny, and finally, by Rambo the Ram, who butts him into “a big pile of poo!” While the intended audience is pre-school, the author sprinkles in some big words, such as “derrière”, “kersplat”, “skedaddling” and “vindaloo”, that children will enjoy and repeat, but adults will need to explain and pronounce on the first reading. The text is often printed over the illustrations, sometimes in extra-large font to emphasize a point. Sometimes it is part of the illustration. When “Lou found a thicket of blossoming trees,” the words from the phrase “Down came the blossom” float down the page with the blossoms. The illustrations are as much fun as the text. The sheep are most often depicted as balls of white with heads, ears and four small pegs for legs. Lou Pine is a two-dimensional creature, while Rambo the Ram blows steam out his nose and wears boxing gloves. The sheep occasionally have glasses, bow-ties or hair-bows. Because the rhyme is jaunty and the illustrations are fun and inventive, this will be a book that small children will want to have read to them over and over. Highly recommended for public and elementary school libraries. Recommendation: 4 stars out of 4Reviewer: Sandy CampbellSandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.053 |
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".