Bibliographic record
Abstract
Seeger, Laura Vaccaro. Green. New York: Roaring Brook Press, 2012. Print. The book, Green reminds us that green is a pervasive colour in the world. Like her book, Black, White, Day, Night: A Book of Opposites, the die cuts will astonish you as two leaves turn into fish on the next page, making you anxious to flip the pages quickly to view the next image. But refrain from flipping too quickly or you will miss the beauty of the acrylic paintings on each page. Only gather a couple children around you as you read, or they will fight over who gets to have it in their arms. With only 38 words, the illustrations need to be looked at carefully. Allow time for their eyes to take in all the details. Be prepared to stop and go back to the previous page because they will realize that somehow they missed something important. Don’t be surprised if the children grab for the lime, reach down to smell the flower, or are scared by the tiger. They will rub the pages; they will feel the texture and might be disappointed that the page is smooth. Younger students will enjoy the predictability of the word ‘green’ on each page. It won’t take them long to recite the book with you, with only two or three syllables per page. Older students will be inspired by the art work and could use the book as an exemplar for an art project. Other students could pick a different colour and find words and pictures to describe the different hues of their chosen colour. Green could be used in science class, to discuss how each hue is created. I would love to see Ms. Seeger create a whole series of colour books!! Highly Recommended: 4 out of 4 Stars Reviewer: Ortensia Norton Ortensia Norton is a Teacher Librarian with Edmonton Public Schools. She is currently enrolled in the TL-DL program through the University of Alberta. She thinks that her jobs as mom and librarian are the best jobs in the world because you get to see the delight on children's faces when they fall in love with a book.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.226 | 0.203 |
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".