Bibliographic record
Abstract
Folger, Napatsi. Joy of Apex. Iqaluit: Inhabit Media, Inc., 2011. Print. This is a first novel from Napatsi Folger, who grew up in Iqaluit, moved to Vancouver and then returned as an adult to work in Iqaluit. She has given us a bright new heroine in Joy, a ten-year old girl who lives in Apex, just outside of Iqaluit in Nunavut. Joy, who tells the story in the first person, is quite a normal pre-teen. She generally wants to do well, but occasionally does silly things like calling little sister, Allusha, by the nickname “ALLA SUCKS”. From the beginning of the novel, it is clear that Joy has a heavier than normal family workload for a ten-year old. Her dad is doing most of the cooking and cleaning and Joy and her older brother, Alex, are looking after each other and Alla. They look out for each other at school, make their own Halloween costumes and solve their own problems. Their mother, Mary, who spends most of her time with her sisters, does come home occasionally. When she does, she and her husband fight, with predictable effects on the children. “They are fighting again. This time it sounds like a big one. The yelling wakes Allusha and we are so frightened that we creep into Alex’s room and crawl into bed with him. After Alex and I calm Allusha’s quite sobs, the three of us sleep together in a little nest for the rest of the night.” Joy’s voice is authentic for a ten year old, who is doing her best to cope while her parents’ marriage falls apart. It is hard not to be moved by her humiliation, anger and pain when her best attempts fail and there is no mom to prevent the disaster or pick up the pieces. Folger’s humour often provides some relief from the weight of the difficulties that Joy faces. For example, when Allusha eats too much candy, throws up and bursts the capillaries in her face, she tells her mother, “Don’t worry, Mama. It’s just burst caterpillars in my face.” This is a well-written volume and an engaging read. The work is clearly rooted in the author’s personal experience growing up in the Eastern Arctic. Pre-teens through adult readers will enjoy this book. Highly recommended for public libraries and school libraries everywhere. Reviewer: Sandy CampbellHighly recommended: 4 stars out of 4Sandy 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 | 0.000 | 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 | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.036 |
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".