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Record W2283938368 · doi:10.20361/g22c83

Belle & Boo and the Yummy Scrummy Day by M. Sutcliffe

2014· article· en· W2283938368 on OpenAlexvenueaboutno aff
Joycelyn Jaca

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAdventureGirlReading (process)ArtPsychologyMedia studiesAdvertisingVisual artsSociologyArt historyPhilosophyLinguistics

Abstract

fetched live from OpenAlex

Sutcliffe, Mandy. Belle & Boo and the Yummy Scrummy Day. Sydney: Orchard Books. 2013. Print.This beautifully illustrated book by Mandy Sutcliffe is about a little girl named Belle and her fussy-eater but funny friend bunny, Boo. Belle and Boo have many other fun-filled days of adventure and discovery, but in this one, the duo explores the orchard and the kitchen.Boo loves cakes and has all the excuses in the world to not eat nutritious food. Belle, on the other hand is a calm but “sneaky” cook who never forces her friend to eat healthy. Instead, she finds a way to lead Boo into trying yummy scrummy fruity things that are not cake!The story is simple, interesting and funny. It attempts to encourage kids to eat healthier but it does not lecture or preach so it is not at all boring. ``Trying things first before saying you don`t like it`` could be the take-away message that young readers will get from this book.The language is age-appropriate and the length of the story is just right. The illustration is vintage-inspired and the colors used made each page attractive and pleasing to the eyes.Belle and Boo and the Yummy Scrummy Day is a book that elementary school libraries and public libraries should have. Parents with young children could definitely add this title to their bedtime stories booklist.Highly recommended: 4 stars out of 4 Reviewer: Joycelyn JacaJoycelyn Jaca is a medical librarian with Alberta Health Services. She is a mother of three girls and is a frequent visitor of public libraries and bookstores to find children’s books.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.851
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.218
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2014
Admission routes2
Has abstractyes

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