Fifteen Dollars and Thirty-Five Cents: A Story About Choices by K. Cole
Bibliographic record
Abstract
Cole, Kathryn. Fifteen Dollars and Thirty-Five Cents: A Story About Choices. Illus. Qin Leng. Second Story Press: Toronto, 2015. Print.Fifteen Dollars and Thirty-Five Cents. A Story About Choices is part of the I am a Great Little Kid Series by Second Story Press, published in conjunction with Boost Child & Youth Advocacy Centre.Fifteen Dollars and Thirty-Five Cents tells a simple story, yet successfully brings across some bigger themes. It takes place at an elementary school. Joseph, who is with his friend Devon, finds some money in the school yard- fifteen dollars and thirty-five cents. He is very excited and wants to keep it, but Devon has second thoughts and thinks that someone they know might have lost it and that they should turn it in. Joseph says “finders’ keepers” and offers Devon five of the dollars not to tell. Devon is very tempted but then sees his classmate Lin crying and has the feeling that it is her money they have found. His suspicion is confirmed when they are back in the classroom and the teacher, Ms. Crosby, asks sad Lin what is wrong. Lin’s friend Claire says it was money for her mother’s birthday present that she has been saving for weeks. Devon is increasingly uncomfortable but Joseph pressures him to stay quiet because they are friends and because he is also worried that people will think he stole it. The teacher suspects something and gives Devon a chance to explain what has happened. He doesn’t tell on his friend but lets his teacher know her suspicions are correct. Ms. Claire comes up with a way for Devon and Joseph to give the money back without them getting in trouble and everyone is happy with the outcome.The story is simple but gets the point across about making the right choices, about friendship, honesty, and “doing the right thing”, without being too preachy. The cast of characters are diverse and the issues dealt with in the story are ones that kids can relate to.The illustrations by Qin Leng are vibrant and colourful and enhance the telling of the story. I would recommend this book for young children from ages 5-8 years of age.Highly recommended: 4 out of 4 starsReviewer: Patti SherbaniukPatti is a Liaison Librarian at the Winspear School of Business at the University of Alberta. She holds a BA in English and an MLIS, both from the University of Alberta. She is passionate about food, travel, the arts and reading books of all shapes and sizes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".