Bibliographic record
Abstract
Deal, Laura. How Nivi Got Her Names. Illus. Charlene Chua. Inhabit Media, 2017.Inhabit Media, an Inuit-owned publishing company, has brought to publication another wonderful story celebrating Inuit naming customs and family ties in How Nivi Got Her Names. The story follows young Nivi as she asks her mother how she came to have the names that she has which leads to an explanation of the traditional Inuit naming practices. The introduction by Aviaq Johnston features some basic information into the cultural background surrounding Inuit custom adoption and naming which would be useful to both adults reading to children and educators looking to frame this book within a lesson plan on Inuit ways of life. Similarly, the glossary featured at the end of the book provides readers with aid in translating the various traditional kinship terms that are used throughout the story.It features 32 pages of full-colour illustrations, all of which are vibrant and provide visual interest for readers while the writing is always on a white background to ensure good readability. The text overall is simple and comprehensible to the intended audience of 5 to 7 year-olds, but may be above their reading level, so an adult may need to read this book aloud. For parents, this book could be a great springboard into discussing family stories with their child. For educators and public librarians, this book is a very accessible introduction to a facet of Inuit culture that could easily be used during reading times or in the classroom.Highly recommended: 4 stars out of 4Lorisia MacLeod is a second year Masters of Library and Information Studies student and Indigenous Intern at the University of Alberta. When not working on her studies, Lorisia enjoys reading almost any variation of Sherlock Holmes or travelling.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.106 | 0.100 |
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".