Bibliographic record
Abstract
Christopher, Neil. Those That Cause Fear. Inhabit Media, 2016.Neil Christopher, who has spent 16 years working to preserve Inuit traditional tales in the Eastern Arctic has, with the work of illustrator Germaine Arnaktauyok, created a field guide to scary creatures of the Arctic. The book shows 20 creatures of whom we should be afraid. Each creature is shown in a full page image created by Arnaktauyok, while the facing page presents Christopher’s description. At the end of the book there is a pronunciation guide for the creatures’ names. Arnaktauyok’s prints are simple designs in shades of browns and greens. The images capture the essence of the stories. For example, the image of the sleeping giant (p. 22) shows how a sleeping giant could be mistaken for a hill. The text is conversational, often addressing the reader as “you." Many of the descriptions open with a question to engage the reader or ask the reader to imagine something. “Have you ever imagined seeing a giant? Do you think you would be scared?” Often the stories contain bits of the traditional knowledge that allowed the Inuit to survive over the millennia. For example, the Kajjait, we are told are the hungry spirits of animals that have been killed and the meat “not used properly” or “allowed to spoil." The spirits look like gaunt wolves. They roam the tundra, eating anything they can find, but in spite of that are constantly starving. The lesson that hunters must use the animals that they kill is clear.While the book is aimed at an upper elementary audience, the content will be valuable to anyone who wants to know about these scary creatures.Inhabit Media has produced a number of books dedicated to individual scary creatures, such as the Qallupilluk and the Amautalik, but this is the first extensive collection. Highly recommended for school and public libraries.Highly recommended: 4 stars out of 4 Reviewer: 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.092 | 0.055 |
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".