Bibliographic record
Abstract
Black, Michael I, and Debbie R. Ohi. I'm Sad. Simon & Shuster Books for Young Readers, 2018. This book is about a sad flamingo and his friends, a girl and a potato, who try to cheer him up. This absurd collection of characters talk about whether or not flamingo will always feel sad and what makes them feel less sad. Much of the dialog is silly. When the potato says that he knows what cheers him up, the picture is of a happy potato and the word “DIRT!!!” in giant letters. Coming after a discussion of ice cream as a “cheer me up”, “dirt” is unexpected and funny. At the end of the book the flamingo asks, “Will you still like me if I’m sad again tomorrow?” The potato responds with an almost nasty, “I don’t even like you now.” This response is meant as a joke and the next two pages show uproarious laughter. However, young children may not understand that it is not usually an appropriate response and some people would find it hurtful. Debi Ridpath Ohi’s simple illustrations do a good job of presenting expressions and emotions. There are often broken black lines around the images, which, strangely, make the characters, particularly the flamingo, look like they are constantly trembling. Apart from that, the images are fun. The most amusing is the one showing the potato as a fourth scoop of ice cream on a cone, with whipped cream and a cherry on top. This book might give a sad young child a few moments of laughter and in the end delivers the message that it’s OK to feel a little bit sad. With these two thoughts in mind, this book is recommended for libraries for young children: daycares, schools, and public libraries. Recommended: 3 out of 4 starsReviewer: Sean Borle Sean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.251 | 0.212 |
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".