Bibliographic record
Abstract
Goose, Roy & McCluskey, Kerry. Sukaq and the Raven. Illustrated by Soyeon Kim. Inhabit Media, 2017. Inhabit Media is a quality publisher and Sukaq and the Raven matches their usual exemplary quality of story and imagery. The story is a traditional legend from Inuit storyteller Roy Goose illustrated using Kim’s beautiful three-dimensional dioramas. This wondrous illustration style previously earned Kim the Amelia Frances Howard-Gibbon Illustrator’s Award for her work You Are Stardust and it is easy to see how her artwork is award-winning. The depth created by the illustrations perfectly complements the story which follows Sukaq as he falls into his favourite bedtime story—how the raven created the world. As with many of Inhabit Media’s works, this story is distinctly Inuit while remaining understandable to everyone which makes it extremely useful in classrooms and libraries. The audience for this piece could range from pre-reading children to later elementary students as the full-page illustrations provide enough interest to any reader. Most young readers will need a reading buddy due to the amount of text and the complexity of some words. Artistically-minded readers may be intrigued by the three-dimensional diorama illustration style though educators or librarians may find this story to be a great introduction to a craft program involving dioramas. Parents may also find this story works well as a bedtime story due to the flow and lack of interrupting onomatopoeias (boom, beep, etc.). I highly recommend this book given how the illustrations and story combine to create a book that is pleasing to readers of many ages. Highly recommended: 4 stars out of 4 Reviewer: Lorisia MacLeod Lorisia MacLeod is an Instruction Librarian at NorQuest College Library and a proud member of the James Smith Cree Nation. When not working on indigenization or diversity in librarianship, Lorisia enjoys reading almost any variation of Sherlock Holmes, comics, or travelling.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.173 | 0.070 |
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".