Bibliographic record
Abstract
Qitsualik-Tinsley, Rachel and Sean Qitsualik-Tinsley. Lesson for the Wolf. Illus. Alan Cook. Iqaluit, NU: Inhabit Media, 2015. Print.The writing duo of Rachel and Sean Qitsualik-Tinsley are back with a story about being comfortable in your own skin, literally. The wolf in this story is not happy to be a wolf, so with the help of “the magic of the land”, he acquires owl’s feathers, the wolverine’s tail and the caribou’s antlers. But he cannot fly like the owl or eat lichen like the caribou and he is too different from the wolves, so he becomes sad, lonely and starves. Eventually he learns the beauty of being himself and the magic of the land restores him. The story is a lovely Arctic fable on the lesson of being true to one’s self. Alan Cook’s paintings capture the wildness of the Arctic, with sweeping brush-strokes and suggestions of distant landscapes. The animals are all cartoon-like creatures, sometimes verging on caricature. Both the wolves and the caribou are drawn with over-accentuated face length and extreme thinness of the abdomen. Children who are struggling to be satisfied with and confident in their identities may be able to identify with the wolf. This book would be a good starting point for discussion. Highly recommended for elementary school libraries and public libraries.Highly recommended: 4 stars out of 4Reviewer: Sandy CampbellSandy 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.
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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 | 0.001 | 0.002 |
| 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 | 0.005 |
| Insufficient payload (model declined to judge) | 0.135 | 0.096 |
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".