Bibliographic record
Abstract
Florence, Melanie. Rez Rebel. James Lorimer & Company, 2017.Narrated by Floyd Twofeathers, a young Cree teenager living on the fictional Bitter Lake Reserve, Rez Rebel sheds important and much-needed light onto the suicide crisis faced by Indigenous communities across Canada.Suicides on Bitter Lake Reserve are rampant. Though Floyd occupies a position of status in his community, with his father being the hereditary chief and his mother a traditional healer, he is not immune from the tragedy—his best friend Aaron had committed suicide. However, after a suicide pact leaves four young girls dead, Floyd’s father struggles to find a solution to help his community and its young people find opportunities and support. Floyd and his friends believe that by sharing their own talents and skills (writing, drawing, sports) with their peers, and giving kids the opportunity to learn their traditional knowledge and practices from the Elders, that this will help turn their community around. When Floyd tries to bring these ideas to his father, he is ignored. It is only with a bold act, and another suicide threat that brings about meaningful change on Bitter Lake Reserve.Given the timeliness of the subject matter, and the lack of books on this topic, this is a much-needed novel. However, the overall story itself is a bit uneven. The book opens with the suicide crisis, and the reader can feel the tension and urgency of the situation. There is a lengthy description of Aaron’s suicide, as well as a crisis in Floyd’s family that may be upsetting or triggering to some readers. The middle of the book loses a bit of its focus, with chapters showing Floyd and his friends being teenagers—hanging out, fishing, falling in love—with little connection to the broader plot. The final quarter of the story reconnects the reader with the suicide crisis and Floyd’s solution for his community. The prose and tone are realistic—teenagers will see themselves in the characters, and the language is simple and accessible.This book would do well in the young adult section of public libraries, as well as in junior high and high school libraries. Recommended: 3 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.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.186 | 0.099 |
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".