Bibliographic record
Abstract
Bailey, Linda. Carson Crosses Canada. Illustrated by Kass Reich. Tundra Books of Random House Canada, 2017.In this delightful picture book, Linda Bailey and Kass Reich combine their talents to produce an imaginary cross-Canada tour for little listeners and beginning readers. The work is age appropriate. The maps Reich creates on the front and back end papers present a simplified vision of our coastlines, territories and provinces. Throughout the pages, line drawings and colourful illustrations evoke our mountains, forests, plains and lakes. We see Canada from the highway, the campsite, the lakeshore and seacoast. Urban references are few. The journey starts in Tofino; it ends on an unnamed Newfoundland shore. En route, there is one nod to Winnipeg where the travelers have a cooling romp in the lake of the same name, and another to Quebec City where they feast on a tortière. For the most part, however, the densely populated cities, our usual obsession, are omitted. What the author and illustrator do offer is a heartwarming, humorous and engaging story.Annie Magruder, the central character, journeys eastward to help her ailing sister, Elsie, who lives on the Atlantic coast. Promising Carson, her little dog, a “surprise” when they reach their destination, Annie packs the essentials: camping equipment, baloney sandwiches, dog food, and “Squeaky Chicken” (Squeaky Chicken is the dog’s toy; each time he chews it he gets “a brand new noise”). Carson is both lovable and credible. On the dry plains of Saskatchewan he eats a grasshopper “for dessert.” In the scorching heat of southern Manitoba he droops. In Niagara Falls where Annie buys a souvenir he “leaves a little souvenir of his own,” and when the tide goes out in the Bay of Fundy, he rolls all over the seabed, the “best mud ever.” Annie’s promised “surprise” for Carson is also credible. (No disclosure, here. Read the book.)Kass Reich’s illustrations are a perfect match for Bailey’s text. That the work concentrates on storyline is a gift to the intended audience. That its inherent geography lesson is subtle and evocative (as opposed to blatant and didactic) is totally refreshing. This is a must for Canadian home, school, and public libraries.Reviewer: Leslie AitkenHighly recommended 4 out of 4 starsLeslie Aitken’s long career in librarianship involved selection of children’s literature for school, public, special, and university collections. She is a former Curriculum Librarian at the University of Alberta.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.334 | 0.147 |
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".