Bibliographic record
Abstract
Oestreich, Laurie. Gluten Free is Part of Me. Illustrated by Kayla Gartenberg. Mascot Books, 2018. Gluten Free Is Part of Me follows a young girl as she describes what it is like living with celiac disease. It presents information in an sensitive way, anticipating and empathizing with children living with celiac disease. The little girl in the story talks about how it can be difficult to attend events where she cannot eat the same foods as other children and how she wishes her celiac disease would go away. She also describes the strategies she uses to stay healthy and to educate those around her. The story is written in verse with simple rhymes and age-appropriate vocabulary for young readers. Author Laurie Oestreich is a New York-based child and family therapist whose inspiration for Gluten Free is Part of Me was her granddaughter who was diagnosed with celiac disease. She presents an overall positive message, helping children to understand celiac disease and how to manage it successfully. Oestreich uses her story to put celiac disease in the context of a variety of health conditions, such as food and environmental allergies, visual and hearing deficits that people experience. Importantly, the story reinforces to young readers that a health condition is merely one aspect of their being, and not what defines them. At the end of the story, a Note From the Doctor, written by physician, Dr Peter Green, director of Columbia University’s Celiac Disease Center, endorses and reinforces the story’s messages. The colourful illustrations accompanying the story, complement and enhance the text. Children will enjoy re-reading the story to examine small details in the illustrations, such as the notes on the bulletin board. Artist and photographer, Kayla Gartenberg’s strategic inclusion of anatomical details in her illustrations, introduces young children to body parts and medical vocabulary relevant to the digestive system and to celiac disease. Highly Recommended: 4 out of 4 stars Reviewer: Maria Tan Maria is a health sciences librarian at the University of Alberta and a former editorial team member of the Deakin Review. She is the co-author, with Sandy Campbell, of A Selective Collection of Children’s Health Fiction 2014-2016, which is an update of the Children’s Health Fiction Checklist presented in the October 2014 Special Issue of the Deakin Review (Vol. 4, No. 2).
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.055 |
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".