Bibliographic record
Abstract
Hill, Janet. Miss Moon, Wise Words from a Dog Governess. Illus. Janet Hill. Toronto: Tundra Books, 2016. Print.This book is disappointing because it lacks the wise (or whimsical) words that it promises and offers only well-worn clichés, like “practice makes perfect” and “remember your manners.” These trite teachings are not offered as part of a story, but simply as a series of lessons. We are told in the introduction that Miss Wilhelmina Moon learned these lessons when she worked for a time as the “governess to sixty-seven dogs on an island off the coast of France,” but we never learn what led her to these insights. The introduction teases us by suggesting that we are about to read about the adventures of a dog governess with an absurdly large number of charges. Unfortunately, there is no story here so the introduction’s implausible premise just seems odd and the text is nothing more than a list of hackneyed expressions. At the back of the book is a “class photo” of the sixty-seven dogs which is confusing because it seems to suggest that Miss Moon is not a governess in a private household, but a teacher at a school for dogs.Since no story grows out of the clever and original idea of an overburdened dog governess, we are left to focus on the illustrations. Dogs are cute. Children are cute. Illustrations of dogs in various guises—wearing glasses, hats, scarves, capes, or bow ties–and involved in various children’s activities—riding a bicycle, listening to a bedtime story, practicing archery, or taking a bath—can hardly fail to be appealing. Janet Hill’s sometimes adorable illustrations manage to capture something of the character of a range of dog breeds and they are sometimes very cleverly conceived, but they vary significantly in quality and sometimes seem unfocused or unfinished. Even so, the only reason to reach for this book is for the charming illustrations.Recommended with reservations: two stars out of fourReviewer: Linda QuirkLinda taught courses in Multicultural Canadian Literature, Women's Writing, and Children's Literature at Queen's University (Kingston) and at Seneca College (Toronto) before moving to Edmonton to become a librarian at Bruce Peel Special Collections & Archives at the University of Alberta. Her favourite children's book to teach is Hana's Suitcase, not only because Hana's story is so compelling, but because the format of this non-fiction book teaches students of all ages about historical investigation and reveals that it is possible to recover the stories of those who have long been forgotten by history.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.136 | 0.114 |
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".