Bibliographic record
Abstract
Walsh, Melanie. Isaac and His Amazing Asperger Superpowers! Candlewick Press, 2016.This picture book is designed to help children better understand children who are on the Asperger’s/autism spectrum. Isaac, like many children with Asperger’s Syndrome, has symptoms that include needing to fidget, sensitivity to sound, exceptional memory for certain kinds of facts, and lack of verbal filters. Instead of making these as negative attributes, Melanie Walsh has used the “superhero” concept as a vehicle for their positive presentation. Telling the story in the first person allows Isaac to directly describe for the reader what his life is like. This allows readers to empathize more easily. For example, he says: “Because I’m a superhero, I have lots of things to think about. I try to remember to be friendly and say hello to people I know, but sometimes I forget. I’m not being rude.” The artwork is brightly coloured. The images are simple and easy to understand, so it does not distract from the story. This would be a good book to read out loud and discuss in a class where there is a child with Asperger’s. While it may not exactly represent all children with Asperger’s, it is a good generalization and will help other children be more accepting of others who have these “superhero” behaviours.Highly Recommended: 4 stars out of 4Reviewer: Sean BorleSean Borle is a University of Alberta undergraduate student who is an advocate for child health and safety.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.088 | 0.061 |
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".