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Record W2741154187 · doi:10.20361/g21x08

The Music in George's Head: George Gershwin Creates Rhapsody in Blue by S. Slade

2017· article· en· W2741154187 on OpenAlexvenueaboutno aff
Sean Borle

Bibliographic record

VenueThe Deakin Review of Children s Literature · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeorge (robot)Palette (painting)Art historyBiographyMelodyArtHead (geology)BrotherMusicalVisual artsLiteratureSociologyGeology

Abstract

fetched live from OpenAlex

Slade, Suzanne. The Music in George's Head: George Gershwin Creates Rhapsody in Blue. Calkins Creek, 2016.This biography gives Gershwin’s early history and then focuses on how he came to write Rhapsody in Blue, beginning with a train ride where the “train noises created new melodies in his head.”This is a very blue book. The whole palette is dark blue, purple and black, with sepia and light brown backgrounds and highlights. The images are surreal. One shows Gershwin reaching out of a bus window to grab a note floating in the air. Elongated keyboards twist and wave their way through collections of overlapping images. Parts of pictures are disproportionate. Apart from the colour and the images, the most striking thing is the shape of the text. On every page some words are much larger, in different fonts and different shades of blue. The text and the images are meant to reflect the wild, unpredictable and jazzy nature of Gershwin’s music.The music message of this book is that composers and musicians can find music anywhere and that great compositions often break the rules.While this is a picture book, the text includes some difficult words like “rhapsody” and “syncopated”, which could be read and understood by children in upper elementary and junior high school, but they might pose a challenge for younger children. Highly Recommended: 3 stars out of 4Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.256
Teacher spread0.223 · 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
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
Published2017
Admission routes2
Has abstractyes

Explore more

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