Bibliographic record
Abstract
Christopher, Neil. The Dreaded Ogress of the Tundra. Iqaluit: Inhabit Media, 2015. PrintAmautaliit are giant ogresses who eat small children. They roam the Arctic tundra looking for unsupervised children such as orphans or those who have wandered away from camp. They sneak up on the children, capture and carry them away in their disgusting baskets containing rotting seaweed and giant bugs. These stories have two themes. First, they are cautionary tales designed to keep children from wandering away from camps and villages. Second, they usually show the children using their ingenuity or ancient magic to escape the not-too-smart amautaliit.This is an updated and revised version of Christopher’s 2009 volume, Stories of the Amautalik, which contains versions of the two stories presented in this work. However, this edition of the book is more like a junior handbook to amautaliit (plural of amautalik). While this book has many illustrations which are appropriately dark, scary and creepy, there is much more text than one usually finds in an Inhabit Media book. At least half of the pages are full text and like Stories of the Amautalik, the reading level is high for young children. The book includes a seven-page introduction to amautaliit, which describes who these creatures are, their clothing, their baskets, their caves and how they hunt small children. At the end of the book there is an “Other Ogres and Ogresses” section, which gives single page, illustrated descriptions of similar creatures, including a giant spider that assumes a human-like form. Even though this is a revision of an earlier work that many libraries will have, the expanded content would make it a useful addition to libraries with children’s collections, and particularly to academic libraries that collect works on Arctic myths and legends.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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".