Bibliographic record
Abstract
McAllister, Paul. A New Song for Herman. Herman’s Monster House Publishing, 2017.There are many books designed to help children who are afraid of monsters. This is the second book that Paul McAllister has written on the subject. In this one, Herman, a green House Monster turned Barista Monster, works at Sarah’s cafe and is famous for his mochaccinos. However, his work is suffering because he is being kept awake at night by an Attic Monster. It turns out that the Attic Monster is just baking cookies in an old Easy Bake Oven, so Herman offers him a job baking at the restaurant. The text is simple, but includes some repetition of the Attic Monster’s song “humba rumba lumba rumba gurgle gurgle bing!”, which children will enjoy and will want to repeat during a reading. Both the text and the illustrations help children identify with monsters rather than being afraid of them. Emily Brown has made the monsters look like cuddly stuffy toys. Even the Attic Monster who scares Herman turns out to be “the cutest little monster he’d ever seen.” Brown has also included fun details in the illustrations. For example, when Herman is at his most sleep deprived, he makes the coffee with dirt and Brown shows him holding a coffee pot that has a flower growing out of it. This fun book is recommended for public and school library collections.Recommended: 3 stars out of 4Reviewer: Sean C. BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.348 | 0.214 |
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".