Bibliographic record
Abstract
Shireen, Nadia. Good Little Wolf. New York: Alfred A Knopf. 2011. Print. Good Little Wolf is British illustrator Nadia Shireen’s picture book debut, and a successful one at that. Shireen, who earned an MA in Children’s Book Illustration from Angela Ruskin University in Cambridge, originally planned a career in law but thankfully pursued her passion for illustration and now, authorship. The story begins with the narrator ensuring a group of youngsters, including a red-hooded girl and a (soother) suckling pig are all comfortable. Rolf is a good little wolf. He is helpful to his friends, the elderly Mrs. Boggins and Little Pig, eats his vegetables and enjoys baking. One day Rolf meets a Big Bad Wolf, who is clearly surprised by Rolf’s goodness; young children will delight at the Big Bad Wolf sniffing Rolf’s butt to confirm that he is, indeed, a wolf. A few tests are in order to determine his wolf-ness and Rolf fails miserably – until the Big Bad Wolf shows up with Mrs. Boggins and a fork. Suddenly Rolf shows his fierce side and the Big Bad Wolf is going to reform – or so it seems. The quirky illustrations and fresh take on a traditional tale will delight the 4-8 crowd, though parents may need to do some explaining after the final twist when we learn the identity of the narrator . I look forward to Shireen’s next book and won’t have to wait long – “Hey, Presto!” is due out this summer. Recommended: 3 out of 4 stars Reviewer: 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.207 | 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".