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Record W607686598 · doi:10.20361/g24c7b

Colors versus Shapes by M. Boldt

2015· article· en· W607686598 on OpenAlexvenueaboutno aff
Kelly Maxwell

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2015
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsnot available
Fundersnot available
KeywordsRead aloudTheme (computing)Visual artsFriendshipCharacter (mathematics)Product (mathematics)ArtComputer sciencePsychologyReading (process)LinguisticsMathematicsWorld Wide WebPhilosophyGeometry

Abstract

fetched live from OpenAlex

Boldt, Mike. Colors versus Shapes. New York: Harper Collins, 2014. Print.Mike Boldt, a local Alberta author/illustrator, creates a carefully scripted theatrical scene in which colours and shapes vie to be the main character or star in a new book hosted by the characters from his first book, 123 versus ABC. The interplay between the colours and shapes is witty, entertaining and educational. Mike Boldt presents the dialogue in colored speech balloons, which allows for a great read aloud experience for the young audience which it is intended. A further benefit of this fun picture book is the introduction and explanation of the mixing of colours and the difference of shapes. This feature makes this book an excellent tool for young students to learn how primary colors combine to make secondary and tertiary colors. Additionally, it enables students to discover the names of different 2D shapes as the shapes morph through the addition of sides. Bantering and competition heat things up until both groups learn that working together is much more productive than working apart. Children will enjoy exploring the final product of such creative collaboration.In all, Colors versus Shapes is a fun educational picture book, full of colourfully illustrated pages with the theme of friendship as a the final lesson to be learned. Highly recommended: 4 out of 4 stars.Reviewer: Kelly Maxwell

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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.

Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2015
Admission routes2
Has abstractyes

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