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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".