Bibliographic record
Abstract
Simpson, Kate. Finding Granny: We Never Really Lose the People We Love. EK Books, 2018. This is an uplifting picture book about a child, Edie, whose beloved grandmother has a stroke and spends a long time recovering in hospital. At first, Edie does not accept that the woman in the bed, whose words do not make sense, is her grandmother. Through art therapy classes that Edie shares with her grandmother, she slowly rebuilds and reaffirms their close relationship. Edie comes to understand that while the colours in Granny’s painting are “all running into each other”, the painting is still beautiful. Gwyenneth Jones’ artwork is bright, exuberant and makes a sad and frightening subject engaging and entertaining. When the doctor, who has bright red glasses and a giant nose, explains that Granny’s “brain isn’t working the way it used to,” she points to a huge picture of a brain that has tiny eyes, a downturned mouth and huge adhesive bandages in an X across it. This book celebrates intergenerational love and the effectiveness of art therapy in healing both relationships and illness. It also offers a lesson of patience and hope for those whose loved ones are recovering from stroke. Finding Granny would be a good addition to public, school, and hospital libraries. Highly Recommended: 4 out of 4 starsReviewer: Sandy Campbell Sandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.074 | 0.053 |
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".