Bibliographic record
Abstract
Florence, Melanie. Stolen Words. Illus. Gabrielle Grimard. Second Story Press, 2017.Stolen Words is a sensitive and thoughtful story about the legacy and intergenerational impact of Canada’s residential school system, the reclamation of language, and the tender relationship between a seven-year-old nôsisim (granddaughter) and her nimosôm (grandfather). Florence draws on her own experience for the story, having never had the opportunity to speak to her own nimosôm about his Cree heritage.After making a dreamcatcher in school, Nôsisim goes to her nimosôm, asking how to say “grandfather” in Cree. Nimosôm replies that he “lost his words a long time ago.” He explains that he (and his words) were taken away from their home, “... to a school that was cold and lonely, where angry white faces raised their voices and their hands when we used our words.” Nôsisim wants to help Nimosôm “find your words again,” and the next day, brings home a Cree dictionary. Reading the long-forgotten words, Nimosôm’s language is reawakened, and he promises Nôsisim that he will teach her his words.Stolen Words does not shy from the realities and long-term impacts of the residential school system. However, Florence addresses these sensitively and age-appropriately. For example, Nimosôm talks about being separated from his family. While the pain of the separation is clear, Florence’s gentle prose ensures it is not overwhelming for young readers. Also, Grimard’s illustrations are evocative, highlighting the close relationship between Nôsisim and Nimosôm, and effectively showing how Nimosôm’s language was captured (and freed) using bird imagery.The phrasing of the sentences, and the inclusion of Cree words makes this a more appropriate read-aloud to younger readers, but it would be suitable for independent reading for students in mid-elementary school. While the subject matter is relevant for students in upper-elementary school, the text itself is below grade reading level. This book is highly recommended for both school and public libraries. Highly recommended: 4 out of 4 starsReviewer: Andrea QuaiattiniAndrea Quaiattini is a Public Services Librarian at the University of Alberta’s JW Scott Health Sciences Library. While working as a camp counsellor, she memorized Mortimer and The Paper Bag Princess by Robert Munsch as bedtime stories for the kids. She can still do all the voices.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.265 | 0.165 |
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".