Bibliographic record
Abstract
Bar-el, Dan. Dream Boats. Illust. Kirsti Anne Wakelin. Vancouver: Simply Read Books, 2013. Print.This is a gorgeously illustrated book which offers readers the opportunity to “float” in a dream boat, travelling deep into the richly-embroidered dreams of children from different lands and varying traditions. The words and images transport us high into the Andes and deep into the rainforests of Haida Gwaii; they take us to the beach in Mumbai and into the city of St. Petersburg; they float on the Niger and sail on the Yangtze. The dream metaphor works well for what is frequently a confusing journey through time and space, through personal narrative and shared folklore, and through the walls that divide us. The central dream of the book is a deeply multicultural dream. One can applaud the author’s intent more than his accomplishment, for it is just a little bit too easy to become quite lost in this dream world in which so many myths and folktales are represented as amalgams or approximations. The saving grace is that the author has inspired the illustrator to visualize an exquisite dream world in which it is a genuine pleasure to be lost.The publisher recommends this book for readers from 4-8 years of age, and this makes sense except that it is difficult to imagine that most children of this age will have enough geographical knowledge or multilayered cultural contexts to grasp the overriding point in even a superficial way.Recommended: 3 out of 4 stars Reviewer: Linda QuirkLinda is Assistant Special Collections Librarian at the Bruce Peel Special Collections Library 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.166 | 0.159 |
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".