Bibliographic record
Abstract
Leach, Sarah. Slug Days. Illustrated by Rebecca Bender. Pajama Press Inc., 2017. On slug days, Lauren feels “slow and slimy." Nothing seems to go her way. Her classmates yell at her. Her teachers are short with her. She feels like she has no friends, and that no one likes her. On butterfly days, however, everything is great; Lauren makes her classmates laugh, works on special project with her mother, and gets to eat her favourite ice cream. As a young girl with Asperger Syndrome, an Autism Spectrum Disorder (ASD), who has trouble understanding social cues and coping with change, Lauren has more slug days than butterfly days. Canadian author Sara Leach and illustrator Rebecca Bender provide invaluable insight into the daily struggles of children living with an ASD. As Lauren navigates through a regular week involving school and time at home, the reader is made aware of the difficulties she faces in effectively communicating with her parents, peers, and teachers. Often taking things literally, Lauren misunderstands directions and advice, which often gets her into trouble. At times, Lauren becomes frustrated and angry. While she recognizes the onset of such feelings, she has trouble controlling her behaviour. Without focusing on the disorder itself, this story provides for the reader a first-person perspective of a child on the spectrum. Bender’s lively illustrations complement the text, increasing comprehension for younger readers. While there are bouts of humour throughout the book, there is a general air of sadness that follows Lauren from the beginning of the story to the end. Apart from Ms. Lagorio who helps Lauren practice conversing with others, the adults in Lauren’s world have a seemingly poor understanding of her condition, as well as her “plan” for avoiding strong emotional reactions and conflict, a reality for many children living with an ASD. This chapter book is highly recommended for public and elementary school libraries, as well as parents and educators alike. Highly Recommended: 4 stars out of 4Reviewer: Victoria Eke Victoria is an Academic Library Resident at the University of Alberta’s John W. Scott Health Sciences Library. Victoria’s interest in health-related children’s literature was piqued during a field trip to the Family Resource Library at The Montreal Children’s Hospital.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.401 | 0.253 |
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".