Bibliographic record
Abstract
Fenske, Jonathan. I’m Fun, Too!. Scholastic Inc., 2018. The children’s book, I’m Fun, Too!, by Jonathan Fenske, is a feel-good book for younger children, teaching life lessons about how each person is special in their own way. The illustrations are done with Lego® characters, which can encourage students to connect with the book if they like using Lego®. This picture book’s target audience is primary students and early learners as there is vocabulary that emphasizes learning about feelings and teaches lessons about sharing and self-worth. It is written in the form of a large comic and has comical aspects to it that will engage students through the colours and funny illustrations. The speech bubbles give the feel of a Lego® comic, making the book more dynamic. This book would be effective at introducing to children how to express their feelings. The Lego® theme creates a setting, where having fun is explored. Younger readers would enjoy the colourful illustrations and the funny aspects of the book, while consequently learning about positive communication. I would recommend this book to students who are in Kindergarten and the first grade. The diction is simplistic, yet also educational, teaching productive ways for kindergarten students to express their emotions and feelings. Recommended: 3 out of 4 starsReviewer: Jack Strouk
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.450 | 0.378 |
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".