Bibliographic record
Abstract
Fishing with Grandma is another lovely book from Nunavut’s Inhabit Media. There are pictures on every page. The overprinted text is a story that describes Inuit ice-fishing practices. Through the dialog between a grandmother and her two grandchildren, we learn details of how to cut holes in the ice, what kinds of lures to use, how far down to drop the line and how to bop a fish on the head to kill it. One of the most important teachings from this book comes at the end, when the children and their grandmother have caught far more Arctic Char than their family needs. On their way home they distribute the fish to people who cannot get out to go fishing. The images tell as much of the story as the text. They are fun and show us small details that are authentic to the environment. The family rides an ATV to the lake while the sled dogs watch, the ATV has a polar bear shaped license plate and when the family gets hot from chipping the ice, they take off their parkas and lay them on the ice. Through the story and the images, we not only learn how to fish, but we also vicariously experience the environment: “I would look up from my fishing hole and listen to the sound of the lake. Ravens flew by, calling “kak, kak. I could also hear Skidoos and ATVs in the distance….”While a valuable contribution documenting ice fishing at a child’s level, the reading level is too high for the intended audience, so for younger children, an adult reader will be required. Overall an excellent book both in terms of content and appeal. Highly recommended for school and public libraries.Highly Recommended: 4 stars out of 4 Reviewer: 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.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.257 | 0.111 |
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".