The Music in George's Head: George Gershwin Creates Rhapsody in Blue by S. Slade
Bibliographic record
Abstract
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.114 | 0.052 |
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".