Bibliographic record
Abstract
Sayres, Brianna K. Where Do Diggers Sleep at Night? Illus. Christian Slade. New York: Random House, 2012. Print.If the title Where Do Diggers Sleep at Night? seems a tad familiar, well, it probably is. In the same vein as the ultra-popular Good Night, Good Night, Construction Site, Diggers presents the sweet nighttime rituals of diggers, trucks and other heavy machinery. At first I thought this was a simple effort to take advantage of a similar, bestselling title however Sayres’ work does indeed hold its own.In this picture book aimed at ages 3-6, first time picture book author Sayres gives young heavy equipment aficionados a delightful take on the bedtime story. In rhyming couplets and often in a humorous manner, all sorts of trucks, cranes and tractors get ready for rest under the watchful headlamps of their caregivers: “Where do garbage trucks sleep / when they’re done collecting trash? / Do their dads sniff their load and say, / ‘Pee-yew—time to take a bath’?” Sure to get the young ones giggling.The sleepy-eyed dozers and tow trucks eventually give way to an equally sleepy young boy in his cozy bed, with a reminder that the trucks will be waiting for him when he wakes. Save for one naughty truck, winking, under the bed (my five-year-old happily pointed this out).Though the illustrations by former Disney animator Christian Slade are rather cartoon-like and not realistic, they match the text well, are in soothing nighttime colours and allow for the trucks to have droopy eyes and smiling faces. Read in a lyrical fashion, or even as a song, this would be a nice end to any wee truck lover’s day. This would make a nice addition to any public library or as a gift.Reviewer: Debbie Feisst Recommended: 3 stars out of 4Debbie is a Public Services Librarian at the H.T. Coutts Education Library at the University of Alberta. When not renovating, she enjoys travel, fitness and young adult fiction.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.079 |
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".