Bibliographic record
Abstract
Johnston, Aviaq. What's My Superpower? Illustrated by Tim Mack, Inhabit Media. 2017. Following her debut novel, Those Who Run in the Sky, Johnston and illustrator Tim Mack have put together the delightful story of a young Inuit girl named Nalvana who sees superpowers in all her friends, but isn't sure if she has one of her own. The book has a bright and colourful style highlighting a young girl's world in the Canadian Territories. Following Nelvana through her story, the reader is introduced to her mother, her unnamed (but adorable) dog, her friends and her community. Her infectious smile and positive attitude come through on every page and as she discovers each of her friends’ superpowers, she, and the reader, begin to wonder about her own. The book mixes Inuktitut terms throughout, and includes a glossary at the end. An excellent introduction to a young girl's world in Northern Canada and with a positive message and vibrant illustrations, the book would work well for young schoolchildren as well as those who might be interested in a view of Canada's different communities and questions about their own superpowers. A great read! Highly recommended: 4 out of 4 stars Reviewer: Kirk MacLeod Kirk is the Open Data Team Lead for the Government of Alberta’s Open Government Portal. A Life-Long reader, he moderates two book clubs and is constantly on the lookout for new great books!
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.140 | 0.099 |
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".