Bibliographic record
Abstract
LaRochelle, David. How Martha Saved her Parents from Green Beans. Illus. Mark Fearing. New York: Dial Books for Young Readers, 2013. Print.This imaginative picture book is a tale that will delight picky eaters and those who live with them. Written in LaRochelle’s signature dramatic hyperbole, this 2014 Minnesota Book Award finalist puts a humorous and absurd twist on the classic ‘eat your vegetables’ dinner time conflict.Despite parental encouragement to eat her beans, Martha knows, “Green beans are bad. Very bad.” She is proved correct when a rogue band of mean green beans swagger into town. They chase old ladies, throw rotten tomatoes, make rude noises and kidnap Martha’s parents. Initially Martha is thrilled. She throws her plate of cold green beans out the window and settles in for a late night of cookies and movies. By morning, however, she misses her parents. When Martha discovers them tied up and surrounded by laughing, dancing villainous beans, she is forced to confront her worst nightmare: Is she “too much of a coward to eat a green bean”?This story is hilariously illustrated by Mark Fearing. The expressive faces of Martha, her parents, those dreaded green beans and even Martha’s dog provide eloquent parallels to the text. While parents may appreciate a conclusion in which the family enjoys alternative vegetables, vegetable haters everywhere will appreciate LaRochelle’s provocative conclusion: “Everyone knows that there is nothing bad about a nice leafy salad.” This boldly illustrated adventure will make an entertaining addition to any public, school or personal library serving young children.Highly Recommended: 4 out of 4 starsReviewer: Shelagh K. GenuisShelagh K. Genuis is an Alberta Innovates–Health Solutions Postdoctoral Research Fellow at the University of Alberta’s School of Public Health. Although an avid reader of biography, she has never stopped reading children’s fiction
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".